Remedying defective knowledge of a knowledge database

ABSTRACT

A method includes detecting a defective entigen group within a knowledge database. The defective entigen group includes entigens and one or more entigen relationships between at least some of the entigens. The defective entigen group represents knowledge of a topic. The method further includes obtaining corrective content for the topic based on the defective entigen group and generating a corrective entigen group based on the corrective content. The method further includes updating the defective entigen group utilizing the corrective entigen group to produce a curated entigen group.

CROSS REFERENCE TO RELATED PATENTS

The present U.S. Utility patent application claims priority pursuant to35 U.S.C. § 119(e) to U.S. Provisional Application No. 62/752,399,entitled “IDENTIFYING BEST PRACTICES BASED ON PLANNING AND OUTCOMECONTENT,” filed Oct. 30, 2018, which is hereby incorporated herein byreference in its entirety and made part of the present U.S. Utilitypatent application for all purposes.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

NOT APPLICABLE

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

NOT APPLICABLE

BACKGROUND OF THE INVENTION Technical Field of the Invention

This invention relates to computing systems and more particularly togenerating data representations of data and analyzing the data utilizingthe data representations.

Description of Related Art

It is known that data is stored in information systems, such as filescontaining text. It is often difficult to produce useful informationfrom this stored data due to many factors. The factors include thevolume of available data, accuracy of the data, and variances in howtext is interpreted to express knowledge. For example, many languagesand regional dialects utilize the same or similar words to representdifferent concepts.

Computers are known to utilize pattern recognition techniques and applystatistical reasoning to process text to express an interpretation in anattempt to overcome ambiguities inherent in words. One patternrecognition technique includes matching a word pattern of a query to aword pattern of the stored data to find an explicit textual answer.Another pattern recognition technique classifies words into majorgrammatical types such as functional words, nouns, adjectives, verbs andadverbs. Grammar based techniques then utilize these grammatical typesto study how words should be distributed within a string of words toform a properly constructed grammatical sentence where each word isforced to support a grammatical operation without necessarilyidentifying what the word is actually trying to describe.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a computingsystem in accordance with the present invention;

FIG. 2 is a schematic block diagram of an embodiment of various serversof a computing system in accordance with the present invention;

FIG. 3 is a schematic block diagram of an embodiment of various devicesof a computing system in accordance with the present invention;

FIGS. 4A and 4B are schematic block diagrams of another embodiment of acomputing system in accordance with the present invention;

FIG. 4C is a logic diagram of an embodiment of a method for interpretingcontent to produce a response to a query within a computing system inaccordance with the present invention;

FIG. 5A is a schematic block diagram of an embodiment of a collectionsmodule of a computing system in accordance with the present invention;

FIG. 5B is a logic diagram of an embodiment of a method for obtainingcontent within a computing system in accordance with the presentinvention;

FIG. 5C is a schematic block diagram of an embodiment of a query moduleof a computing system in accordance with the present invention;

FIG. 5D is a logic diagram of an embodiment of a method for providing aresponse to a query within a computing system in accordance with thepresent invention;

FIG. 5E is a schematic block diagram of an embodiment of an identigenentigen intelligence (IEI) module of a computing system in accordancewith the present invention;

FIG. 5F is a logic diagram of an embodiment of a method for analyzingcontent within a computing system in accordance with the presentinvention;

FIG. 6A is a schematic block diagram of an embodiment of an elementidentification module and an interpretation module of a computing systemin accordance with the present invention;

FIG. 6B is a logic diagram of an embodiment of a method for interpretinginformation within a computing system in accordance with the presentinvention;

FIG. 6C is a schematic block diagram of an embodiment of an answerresolution module of a computing system in accordance with the presentinvention;

FIG. 6D is a logic diagram of an embodiment of a method for producing ananswer within a computing system in accordance with the presentinvention;

FIG. 7A is an information flow diagram for interpreting informationwithin a computing system in accordance with the present invention;

FIG. 7B is a relationship block diagram illustrating an embodiment ofrelationships between things and representations of things within acomputing system in accordance with the present invention;

FIG. 7C is a diagram of an embodiment of a synonym words table within acomputing system in accordance with the present invention;

FIG. 7D is a diagram of an embodiment of a polysemous words table withina computing system in accordance with the present invention;

FIG. 7E is a diagram of an embodiment of transforming words intogroupings within a computing system in accordance with the presentinvention;

FIG. 8A is a data flow diagram for accumulating knowledge within acomputing system in accordance with the present invention;

FIG. 8B is a diagram of an embodiment of a groupings table within acomputing system in accordance with the present invention;

FIG. 8C is a data flow diagram for answering questions utilizingaccumulated knowledge within a computing system in accordance with thepresent invention;

FIG. 8D is a data flow diagram for answering questions utilizinginterference within a computing system in accordance with the presentinvention;

FIG. 8E is a relationship block diagram illustrating another embodimentof relationships between things and representations of things within acomputing system in accordance with the present invention;

FIGS. 8F and 8G are schematic block diagrams of another embodiment of acomputing system in accordance with the present invention;

FIG. 8H is a logic diagram of an embodiment of a method for processingcontent to produce knowledge within a computing system in accordancewith the present invention;

FIGS. 8J and 8K are schematic block diagrams another embodiment of acomputing system in accordance with the present invention;

FIG. 8L is a logic diagram of an embodiment of a method for generating aquery response to a query within a computing system in accordance withthe present invention;

FIG. 9A is a schematic block diagram of another embodiment of acomputing system in accordance with the present invention;

FIG. 9B is a logic diagram of an embodiment of a method for identifyingbest practices within a computing system in accordance with the presentinvention;

FIG. 10A is a schematic block diagram of another embodiment of acomputing system in accordance with the present invention;

FIG. 10B is a logic diagram of an embodiment of a method for providing aquery dashboard within a computing system in accordance with the presentinvention;

FIG. 11A is a schematic block diagram of another embodiment of acomputing system in accordance with the present invention;

FIG. 11B is a logic diagram of an embodiment of a method for processinga suspended query within a computing system in accordance with thepresent invention;

FIGS. 12A-12D are schematic block diagrams of another embodiment of acomputing system illustrating an embodiment of a method for collectingcontent to remedy potentially incomplete and/or incorrect knowledgewithin a computing system in accordance with the present invention;

FIG. 13A is a schematic block diagram of another embodiment of acomputing system in accordance with the present invention;

FIG. 13B is a logic diagram of an embodiment of a method for curatingnew knowledge within a computing system in accordance with the presentinvention;

FIGS. 14A and 14B are schematic block diagrams of another embodiment ofa computing system in accordance with the present invention;

FIG. 14C is a logic diagram of an embodiment of a method for processingcontent to produce knowledge utilizing a confidence level within acomputing system in accordance with the present invention;

FIGS. 15A and 15B are schematic block diagrams another embodiment of acomputing system in accordance with the present invention;

FIG. 15C is a logic diagram of an embodiment of a method for generatinga query response to a query utilizing groupings within a knowledge basewithin a computing system in accordance with the present invention;

FIG. 16A is a schematic block diagram of another embodiment of acomputing system in accordance with the present invention;

FIG. 16B is a logic diagram of an embodiment of a method for generatinga query response to a query utilizing a confidence level within acomputing system in accordance with the present invention;

FIGS. 17A and 17B are schematic block diagrams of another embodiment ofa computing system in accordance with the present invention;

FIG. 17C is a logic diagram of an embodiment of a method for processingcontent to produce knowledge utilizing a certainty level within acomputing system in accordance with the present invention;

FIGS. 18A and 18B are schematic block diagrams of another embodiment ofa computing system in accordance with the present invention;

FIG. 18C is a logic diagram of an embodiment of a method for processingcontent to produce knowledge within a computing system in accordancewith the present invention;

FIGS. 19A and 19B are schematic block diagrams another embodiment of acomputing system in accordance with the present invention; and

FIG. 19C is a logic diagram of an embodiment of a method for generatinga query response to a query within a computing system in accordance withthe present invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic block diagram of an embodiment of a computingsystem 10 that includes a plurality of user devices 12-1 through 12-N, aplurality of wireless user devices 14-1 through 14-N, a plurality ofcontent sources 16-1 through 16-N, a plurality of transactional servers18-1 through 18-N, a plurality of artificial intelligence (AI) servers20-1 through 20-N, and a core network 24. The core network 24 includesat least one of the Internet, a public radio access network (RAN), andany private network. Hereafter, the computing system 10 may beinterchangeably referred to as a data network, a data communicationnetwork, a system, a communication system, and a data communicationsystem. Hereafter, the user device and the wireless user device may beinterchangeably referred to as user devices, and each of thetransactional servers and the AI servers may be interchangeably referredto as servers.

Each user device, wireless user device, transactional server, and AIserver includes a computing device that includes a computing core. Ingeneral, a computing device is any electronic device that cancommunicate data, process data, and/or store data. A further generalityof a computing device is that it includes one or more of a centralprocessing unit (CPU), a memory system, a sensor (e.g., internal orexternal), user input/output interfaces, peripheral device interfaces,communication elements, and an interconnecting bus structure.

As further specific examples, each of the computing devices may be aportable computing device and/or a fixed computing device. A portablecomputing device may be an embedded controller, a smart sensor, a smartpill, a social networking device, a gaming device, a cell phone, a smartphone, a robot, a personal digital assistant, a digital music player, adigital video player, a laptop computer, a handheld computer, a tablet,a video game controller, an engine controller, a vehicular controller,an aircraft controller, a maritime vessel controller, and/or any otherportable device that includes a computing core. A fixed computing devicemay be security camera, a sensor device, a household appliance, amachine, a robot, an embedded controller, a personal computer (PC), acomputer server, a cable set-top box, a satellite receiver, a televisionset, a printer, a fax machine, home entertainment equipment, a cameracontroller, a video game console, a critical infrastructure controller,and/or any type of home or office computing equipment that includes acomputing core. An embodiment of the various servers is discussed ingreater detail with reference to FIG. 2. An embodiment of the variousdevices is discussed in greater detail with reference to FIG. 3.

Each of the content sources 16-1 through 16-N includes any source ofcontent, where the content includes one or more of data files, a datastream, a tech stream, a text file, an audio stream, an audio file, avideo stream, a video file, etc. Examples of the content sources includea weather service, a multi-language online dictionary, a fact server, abig data storage system, the Internet, social media systems, an emailserver, a news server, a schedule server, a traffic monitor, a securitycamera system, audio monitoring equipment, an information server, aservice provider, a data aggregator, and airline traffic server, ashipping and logistics server, a banking server, a financial transactionserver, etc. Alternatively, or in addition to, one or more of thevarious user devices may provide content. For example, a wireless userdevice may provide content (e.g., issued as a content message) when thewireless user device is able to capture data (e.g., text input, sensorinput, etc.).

Generally, an embodiment of this invention presents solutions where thecomputing system 10 supports the generation and utilization of knowledgeextracted from content. For example, the AI servers 20-1 through 20-Ningest content from the content sources 16-1 through 16-N by receiving,via the core network 24 content messages 28-1 through 28-N as AImessages 32-1 through 32-N, extract the knowledge from the ingestedcontent, and interact with the various user devices to utilize theextracted knowledge by facilitating the issuing, via the core network24, user messages 22-1 through 22-N to the user devices 12-1 through12-N and wireless signals 26-1 through 26-N to the wireless user devices14-1 through 14-N.

Each content message 28-1 through 28-N includes a content request (e.g.,requesting content related to a topic, content type, content timing, oneor more domains, etc.) or a content response, where the content responseincludes real-time or static content such as one or more of dictionaryinformation, facts, non-facts, weather information, sensor data, newsinformation, blog information, social media content, user daily activityschedules, traffic conditions, community event schedules, schoolschedules, user schedules airline records, shipping records, logisticsrecords, banking records, census information, global financial historyinformation, etc. Each AI message 32-1 through 32-N includes one or moreof content messages, user messages (e.g., a query request, a queryresponse that includes an answer to a query request), and transactionmessages (e.g., transaction information, requests and responses relatedto transactions). Each user message 22-1 through 22-N includes one ormore of a query request, a query response, a trigger request, a triggerresponse, a content collection, control information, softwareinformation, configuration information, security information, routinginformation, addressing information, presence information, analyticsinformation, protocol information, all types of media, sensor data,statistical data, user data, error messages, etc.

When utilizing a wireless signal capability of the core network 24, eachof the wireless user devices 14-1 through 14-N encodes/decodes dataand/or information messages (e.g., user messages such as user messages22-1 through 22-N) in accordance with one or more wireless standards forlocal wireless data signals (e.g., Wi-Fi, Bluetooth, ZigBee) and/or forwide area wireless data signals (e.g., 2G, 3G, 4G, 5G, satellite,point-to-point, etc.) to produce wireless signals 26-1 through 26-N.Having encoded/decoded the data and/or information messages, thewireless user devices 14-1 through 14-N and/receive the wireless signalsto/from the wireless capability of the core network 24.

As another example of the generation and utilization of knowledge, thetransactional servers 18-1 through 18-N communicate, via the corenetwork 24, transaction messages 30-1 through 30-N as further AImessages 32-1 through 32-N to facilitate ingesting of transactional typecontent (e.g., real-time crypto currency transaction information) and tofacilitate handling of utilization of the knowledge by one or more ofthe transactional servers (e.g., for a transactional function) inaddition to the utilization of the knowledge by the various userdevices. Each transaction message 30-1 through 30-N includes one or moreof a query request, a query response, a trigger request, a triggerresponse, a content message, and transactional information, where thetransactional information may include one or more of consumer purchasinghistory, crypto currency ledgers, stock market trade information, otherinvestment transaction information, etc.

In another specific example of operation of the generation andutilization of knowledge extracted from the content, the user device12-1 issues a user message 22-1 to the AI server 20-1, where the usermessage 22-1 includes a query request and where the query requestincludes a question related to a first domain of knowledge. The issuingincludes generating the user message 22-1 based on the query request(e.g., the question), selecting the AI server 20-1 based on the firstdomain of knowledge, and sending, via the core network 24, the usermessage 22-1 as a further AI message 32-1 to the AI server 20-1. Havingreceived the AI message 32-1, the AI server 20-1 analyzes the questionwithin the first domain, generates further knowledge, generates apreliminary answer, generates a quality level indicator of thepreliminary answer, and determines to gather further content when thequality level indicator is below a minimum quality threshold level.

When gathering the further content, the AI server 20-1 issues, via thecore network 24, a still further AI message 32-1 as a further contentmessage 28-1 to the content source 16-1, where the content message 28-1includes a content request for more content associated with the firstdomain of knowledge and in particular the question. Alternatively, or inaddition to, the AI server 20-1 issues the content request to another AIserver to facilitate a response within a domain associated with theother AI server. Further alternatively, or in addition to, the AI server20-1 issues the content request to one or more of the various userdevices to facilitate a response from a subject matter expert.

Having received the content message 28-1, the contents or 16-1 issues,via the core network 24, a still further content message 28-1 to the AIserver 20-1 as a yet further AI message 32-1, where the still furthercontent message 28-1 includes requested content. The AI server 20-1processes the received content to generate further knowledge. Havinggenerated the further knowledge, the AI server 20-1 re-analyzes thequestion, generates still further knowledge, generates anotherpreliminary answer, generates another quality level indicator of theother preliminary answer, and determines to issue a query response tothe user device 12-1 when the quality level indicator is above theminimum quality threshold level. When issuing the query response, the AIserver 20-1 generates an AI message 32-1 that includes another usermessage 22-1, where the other user message 22-1 includes the otherpreliminary answer as a query response including the answer to thequestion. Having generated the AI message 32-1, the AI server 20-1sends, via the core network 24, the AI message 32-1 as the user message22-1 to the user device 12-1 thus providing the answer to the originalquestion of the query request.

FIG. 2 is a schematic block diagram of an embodiment of the AI servers20-1 through 20-N and the transactional servers 18-1 through 18-N of thecomputing system 10 of FIG. 1. The servers include a computing core 52,one or more visual output devices 74 (e.g., video graphics display,touchscreen, LED, etc.), one or more user input devices 76 (e.g.,keypad, keyboard, touchscreen, voice to text, a push button, amicrophone, a card reader, a door position switch, a biometric inputdevice, etc.), one or more audio output devices 78 (e.g., speaker(s),headphone jack, a motor, etc.), and one or more visual input devices 80(e.g., a still image camera, a video camera, photocell, etc.).

The servers further include one or more universal serial bus (USB)devices (USB devices 1-U), one or more peripheral devices (e.g.,peripheral devices 1-P), one or more memory devices (e.g., one or moreflash memory devices 92, one or more hard drive (HD) memories 94, andone or more solid state (SS) memory devices 96, and/or cloud memory 98).The servers further include one or more wireless location modems 84(e.g., global positioning satellite (GPS), Wi-Fi, angle of arrival, timedifference of arrival, signal strength, dedicated wireless location,etc.), one or more wireless communication modems 86-1 through 86-N(e.g., a cellular network transceiver, a wireless data networktransceiver, a Wi-Fi transceiver, a Bluetooth transceiver, a 315 MHztransceiver, a zig bee transceiver, a 60 GHz transceiver, etc.), a telcointerface 102 (e.g., to interface to a public switched telephonenetwork), and a wired local area network (LAN) 88 (e.g., optical,electrical), and a wired wide area network (WAN) 90 (e.g., optical,electrical).

The computing core 52 includes a video graphics module 54, one or moreprocessing modules 50-1 through 50-N (e.g., which may include one ormore secure co-processors), a memory controller 56 and one or more mainmemories 58-1 through 58-N (e.g., RAM serving as local memory). Thecomputing core 52 further includes one or more input/output (I/O) deviceinterfaces 62, an input/output (I/O) controller 60, a peripheralinterface 64, one or more USB interfaces 66, one or more networkinterfaces 72, one or more memory interfaces 70, and/or one or moreperipheral device interfaces 68.

The processing modules may be a single processing device or a pluralityof processing devices where the processing device may further bereferred to as one or more of a “processing circuit”, a “processor”,and/or a “processing unit”. Such a processing device may be amicroprocessor, micro-controller, digital signal processor,microcomputer, central processing unit, field programmable gate array,programmable logic device, state machine, logic circuitry, analogcircuitry, digital circuitry, and/or any device that manipulates signals(analog and/or digital) based on hard coding of the circuitry and/oroperational instructions.

The processing module, module, processing circuit, and/or processingunit may be, or further include, memory and/or an integrated memoryelement, which may be a single memory device, a plurality of memorydevices, and/or embedded circuitry of another processing module, module,processing circuit, and/or processing unit. Such a memory device may bea read-only memory, random access memory, volatile memory, non-volatilememory, static memory, dynamic memory, flash memory, cache memory,and/or any device that stores digital information. Note that if theprocessing module, module, processing circuit, and/or processing unitincludes more than one processing device, the processing devices may becentrally located (e.g., directly coupled together via a wired and/orwireless bus structure) or may be distributedly located (e.g., cloudcomputing via indirect coupling via a local area network and/or a widearea network).

Further note that if the processing module, module, processing circuit,and/or processing unit implements one or more of its functions via astate machine, analog circuitry, digital circuitry, and/or logiccircuitry, the memory and/or memory element storing the correspondingoperational instructions may be embedded within, or external to, thecircuitry comprising the state machine, analog circuitry, digitalcircuitry, and/or logic circuitry. Still further note that, the memoryelement may store, and the processing module, module, processingcircuit, and/or processing unit executes, hard coded and/or operationalinstructions corresponding to at least some of the steps and/orfunctions illustrated in one or more of the Figures. Such a memorydevice or memory element can be included in an article of manufacture.

Each of the interfaces 62, 66, 68, 70, and 72 includes a combination ofhardware (e.g., connectors, wiring, etc.) and may further includeoperational instructions stored on memory (e.g., driver software) thatare executed by one or more of the processing modules 50-1 through 50-Nand/or a processing circuit within the interface. Each of the interfacescouples to one or more components of the servers. For example, one ofthe IO device interfaces 62 couples to an audio output device 78. Asanother example, one of the memory interfaces 70 couples to flash memory92 and another one of the memory interfaces 70 couples to cloud memory98 (e.g., an on-line storage system and/or on-line backup system). Inother embodiments, the servers may include more or less devices andmodules than shown in this example embodiment of the servers.

FIG. 3 is a schematic block diagram of an embodiment of the variousdevices of the computing system 10 of FIG. 1, including the user devices12-1 through 12-N and the wireless user devices 14-1 through 14-N. Thevarious devices include the visual output device 74 of FIG. 2, the userinput device 76 of FIG. 2, the audio output device 78 of FIG. 2, thevisual input device 80 of FIG. 2, and one or more sensors 82.

The sensor may implemented internally and/or externally to the device.Example sensors includes a still camera, a video camera, servo motorsassociated with a camera, a position detector, a smoke detector, a gasdetector, a motion sensor, an accelerometer, velocity detector, acompass, a gyro, a temperature sensor, a pressure sensor, an altitudesensor, a humidity detector, a moisture detector, an imaging sensor, anda biometric sensor. Further examples of the sensor include an infraredsensor, an audio sensor, an ultrasonic sensor, a proximity detector, amagnetic field detector, a biomaterial detector, a radiation detector, aweight detector, a density detector, a chemical analysis detector, afluid flow volume sensor, a DNA reader, a wind speed sensor, a winddirection sensor, and an object detection sensor.

Further examples of the sensor include an object identifier sensor, amotion recognition detector, a battery level detector, a roomtemperature sensor, a sound detector, a smoke detector, an intrusiondetector, a motion detector, a door position sensor, a window positionsensor, and a sunlight detector. Still further sensor examples includemedical category sensors including: a pulse rate monitor, a heart rhythmmonitor, a breathing detector, a blood pressure monitor, a blood glucoselevel detector, blood type, an electrocardiogram sensor, a body massdetector, an imaging sensor, a microphone, body temperature, etc.

The various devices further include the computing core 52 of FIG. 2, theone or more universal serial bus (USB) devices (USB devices 1-U) of FIG.2, the one or more peripheral devices (e.g., peripheral devices 1-P) ofFIG. 2, and the one or more memories of FIG. 2 (e.g., flash memories 92,HD memories 94, SS memories 96, and/or cloud memories 98). The variousdevices further include the one or more wireless location modems 84 ofFIG. 2, the one or more wireless communication modems 86-1 through 86-Nof FIG. 2, the telco interface 102 of FIG. 2, the wired local areanetwork (LAN) 88 of FIG. 2, and the wired wide area network (WAN) 90 ofFIG. 2. In other embodiments, the various devices may include more orless internal devices and modules than shown in this example embodimentof the various devices.

FIGS. 4A and 4B are schematic block diagrams of another embodiment of acomputing system that includes one or more of the user device 12-1 ofFIG. 1, the wireless user device 14-1 of FIG. 1, the content source 16-1of FIG. 1, the transactional server 18-1 of FIG. 1, the user device 12-2of FIG. 1, and the AI server 20-1 of FIG. 1. The AI server 20-1 includesthe processing module 50-1 (e.g., associated with the servers) of FIG.2, where the processing module 50-1 includes a collections module 120,an identigen entigen intelligence (IEI) module 122, and a query module124. Alternatively, the collections module 120, the IEI module 122, andthe query module 124 may be implemented by the processing module 50-1(e.g., associated with the various user devices) of FIG. 3. Thecomputing system functions to interpret content to produce a response toa query.

FIG. 4A illustrates an example of the interpreting of the content toproduce the response to the query where the collections module 120interprets (e.g., based on an interpretation approach such as rules) atleast one of a collections request 132 from the query module 124 and acollections request within collections information 130 from the IEImodule 122 to produce content request information (e.g., potentialsources, content descriptors of desired content). Alternatively, or inaddition to, the collections module 120 may facilitate gathering furthercontent based on a plurality of collection requests from a plurality ofdevices of the computing system 10 of FIG. 1.

The collections request 132 is utilized to facilitate collection ofcontent, where the content may be received in a real-time fashion onceor at desired intervals, or in a static fashion from previous discretetime frames. For instance, the query module 124 issues the collectionsrequest 132 to facilitate collection of content as a background activityto support a long-term query (e.g., how many domestic airline flightsover the next seven days include travelers between the age of 18 and 35years old). The collections request 132 may include one or more of arequester identifier (ID), a content type (e.g., language, dialect,media type, topic, etc.), a content source indicator, securitycredentials (e.g., an authorization level, a password, a user ID,parameters utilized for encryption, etc.), a desired content qualitylevel, trigger information (e.g., parameters under which to collectcontent based on a pre-event, an event (i.e., content quality levelreaches a threshold to cause the trigger, trueness), or a timeframe), adesired format, and a desired timing associated with the content.

Having interpreted the collections request 132, the collections module120 selects a source of content based on the content requestinformation. The selecting includes one or more of identifying one ormore potential sources based on the content request information,selecting the source of content from the potential sources utilizing aselection approach (e.g., favorable history, a favorable security level,favorable accessibility, favorable cost, favorable performance, etc.).For example, the collections module 120 selects the content source 16-1when the content source 16-1 is known to provide a favorable contentquality level for a domain associated with the collections request 132.

Having selected the source of content, the collections module 120 issuesa content request 126 to the selected source of content. The issuingincludes generating the content request 126 based on the content requestinformation for the selected source of content and sending the contentrequest 126 to the selected source of content. The content request 126may include one or more of a content type indicator, a requester ID,security credentials for content access, and any other informationassociated with the collections request 132. For example, thecollections module 120 sends the content request 126, via the corenetwork 24 of FIG. 1, to the content source 16-1. Alternatively, or inaddition to, the collections module 120 may send a similar contentrequest 126 to one or more of the user device 12-1, the wireless userdevice 14-1, and the transactional server 18-1 to facilitate collectingof further content.

In response to the content request 126, the collections module 120receives one or more content responses 128. The content response 128includes one or more of content associated with the content source, acontent source identifier, security credential processing information,and any other information pertaining to the desired content. Havingreceived the content response 128, the collections module 120 interpretsthe received content response 128 to produce collections information130, where the collections information 130 further includes acollections response from the collections module 120 to the IEI module122.

The collections response includes one or more of transformed content(e.g., completed sentences and paragraphs), timing informationassociated with the content, a content source ID, and a content qualitylevel. Having generated the collections response of the collectionsinformation 130, the collections module 120 sends the collectionsinformation 130 to the IEI module 122. Having received the collectionsinformation 130 from the collections module 120, the IEI module 122interprets the further content of the content response to generatefurther knowledge, where the further knowledge is stored in a memoryassociated with the IEI module 122 to facilitate subsequent answering ofquestions posed in received queries.

FIG. 4B further illustrates the example of the interpreting of thecontent to produce the response to the query where, the query module 124interprets a received query request 136 from a requester to produce aninterpretation of the query request. For example, the query module 124receives the query request 136 from the user device 12-2, and/or fromone or more of the wireless user device 14-2 and the transactionalserver 18-2. The query request 136 includes one or more of an identifier(ID) associated with the request (e.g., requester ID, ID of an entity tosend a response to), a question, question constraints (e.g., within atimeframe, within a geographic area, within a domain of knowledge,etc.), and content associated with the question (e.g., which may beanalyzed for new knowledge itself).

The interpreting of the query request 136 includes determining whetherto issue a request to the IEI module 122 (e.g., a question, perhaps withcontent) and/or to issue a request to the collections module 120 (e.g.,for further background content). For example, the query module 124produces the interpretation of the query request to indicate to send therequest directly to the IEI module 122 when the question is associatedwith a simple non-time varying function answer (e.g., question: “howmany hydrogen atoms does a molecule of water have?”).

Having interpreted the query request 136, the query module 124 issues atleast one of an IEI request as query information 138 to the IEI module122 (e.g., when receiving a simple new query request) and a collectionsrequest 132 to the collections module 120 (e.g., based on two or morequery requests 136 requiring more substantive content gathering). TheIEI request of the query information 138 includes one or more of anidentifier (ID) of the query module 124, an ID of the requester (e.g.,the user device 12-2), a question (e.g., with regards to content foranalysis, with regards to knowledge minded by the AI server from generalcontent), one or more constraints (e.g., assumptions, restrictions,etc.) associated with the question, content for analysis of thequestion, and timing information (e.g., a date range for relevance ofthe question).

Having received the query information 138 that includes the IEI requestfrom the query module 124, the IEI module 122 determines whether asatisfactory response can be generated based on currently availableknowledge, including that of the query request 136. The determiningincludes indicating that the satisfactory response cannot be generatedwhen an estimated quality level of an answer falls below a minimumquality threshold level. When the satisfactory response cannot begenerated, the IEI module 122 facilitates collecting more content. Thefacilitating includes issuing a collections request to the collectionsmodule 120 of the AI server 20-1 and/or to another server or userdevice, and interpreting a subsequent collections response 134 ofcollections information 130 that includes further content to producefurther knowledge to enable a more favorable answer.

When the IEI module 122 indicates that the satisfactory response can begenerated, the IEI module 122 issues an IEI response as queryinformation 138 to the query module 124. The IEI response includes oneor more of one or more answers, timing relevance of the one or moreanswers, an estimated quality level of each answer, and one or moreassumptions associated with the answer. The issuing includes generatingthe IEI response based on the collections response 134 of thecollections information 130 and the IEI request, and sending the IEIresponse as the query information 138 to the query module 124.Alternatively, or in addition to, at least some of the further contentcollected by the collections module 120 is utilized to generate acollections response 134 issued by the collections module 120 to thequery module 124. The collections response 134 includes one or more offurther content, a content availability indicator (e.g., when, where,required credentials, etc.), a content freshness indicator (e.g.,timestamps, predicted time availability), content source identifiers,and a content quality level.

Having received the query information 138 from the IEI module 122, thequery module 124 issues a query response 140 to the requester based onthe IEI response and/or the collections response 134 directly from thecollections module 120, where the collection module 120 generates thecollections response 134 based on collected content and the collectionsrequest 132. The query response 140 includes one or more of an answer,answer timing, an answer quality level, and answer assumptions.

FIG. 4C is a logic diagram of an embodiment of a method for interpretingcontent to produce a response to a query within a computing system. Inparticular, a method is presented for use in conjunction with one ormore functions and features described in conjunction with FIGS. 1-3,4A-4B, and also FIG. 4C. The method includes step 150 where acollections module of a processing module of one or more computingdevices (e.g., of one or more servers) interprets a collections requestto produce content request information. The interpreting may include oneor more of identifying a desired content source, identifying a contenttype, identifying a content domain, and identifying content timingrequirements.

The method continues at step 152 where the collections module selects asource of content based on the content request information. For example,the collections module identifies one or more potential sources based onthe content request information and selects the source of content fromthe potential sources utilizing a selection approach (e.g., based on oneor more of favorable history, a favorable security level, favorableaccessibility, favorable cost, favorable performance, etc.). The methodcontinues at step 154 where the collections module issues a contentrequest to the selected source of content. The issuing includesgenerating a content request based on the content request informationfor the selected source of content and sending the content request tothe selected source of content.

The method continues at step 156 where the collections module issuescollections information to an identigen entigen intelligence (IEI)module based on a received content response, where the IEI moduleextracts further knowledge from newly obtained content from the one ormore received content responses. For example, the collections modulegenerates the collections information based on newly obtained contentfrom the one or more received content responses of the selected sourceof content.

The method continues at step 158 where a query module interprets areceived query request from a requester to produce an interpretation ofthe query request. The interpreting may include determining whether toissue a request to the IEI module (e.g., a question) or to issue arequest to the collections module to gather further background content.The method continues at step 160 where the query module issues a furthercollections request. For example, when receiving a new query request,the query module generates a request for the IEI module. As anotherexample, when receiving a plurality of query requests for similarquestions, the query module generates a request for the collectionsmodule to gather further background content.

The method continues at step 162 where the IEI module determines whethera satisfactory query response can be generated when receiving therequest from the query module. For example, the IEI module indicatesthat the satisfactory query response cannot be generated when anestimated quality level of an answer is below a minimum answer qualitythreshold level. The method branches to step 166 when the IEI moduledetermines that the satisfactory query response can be generated. Themethod continues to step 164 when the IEI module determines that thesatisfactory query response cannot be generated. When the satisfactoryquery response cannot be generated, the method continues at step 164where the IEI module facilitates collecting more content. The methodloops back to step 150.

When the satisfactory query response can be generated, the methodcontinues at step 166 where the IEI module issues an IEI response to thequery module. The issuing includes generating the IEI response based onthe collections response and the IEI request, and sending the IEIresponse to the query module. The method continues at step 168 where thequery module issues a query response to the requester. For example, thequery module generates the query response based on the IEI responseand/or a collections response from the collections module and sends thequery response to the requester, where the collections module generatesthe collections response based on collected content and the collectionsrequest.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIG. 5A is a schematic block diagram of an embodiment of the collectionsmodule 120 of FIG. 4A that includes a content acquisition module 180, acontent selection module 182, a source selection module 184, a contentsecurity module 186, an acquisition timing module 188, a contenttransformation module 190, and a content quality module 192. Generally,an embodiment of this invention presents solutions where the collectionsmodule 120 supports collecting content.

In an example of operation of the collecting of the content, the contentacquisition module 180 receives a collections request 132 from arequester. The content acquisition module 180 obtains content selectioninformation 194 based on the collections request 132. The contentselection information 194 includes one or more of content requirements,a desired content type indicator, a desired content source identifier, acontent type indicator, a candidate source identifier (ID), and acontent profile (e.g., a template of typical parameters of the content).For example, the content acquisition module 180 receives the contentselection information 194 from the content selection module 182, wherethe content selection module 182 generates the content selectioninformation 194 based on a content selection information request fromthe content acquisition module 180 and where the content acquisitionmodule 180 generates the content selection information request based onthe collections request 132.

The content acquisition module 180 obtains source selection information196 based on the collections request 132. The source selectioninformation 196 includes one or more of candidate source identifiers, acontent profile, selected sources, source priority levels, andrecommended source access timing. For example, the content acquisitionmodule 180 receives the source selection information 196 from the sourceselection module 184, where the source selection module 184 generatesthe source selection information 196 based on a source selectioninformation request from the content acquisition module 180 and wherethe content acquisition module 180 generates the source selectioninformation request based on the collections request 132.

The content acquisition module 180 obtains acquisition timinginformation 200 based on the collections request 132. The acquisitiontiming information 200 includes one or more of recommended source accesstiming, confirmed source access timing, source access testing results,estimated velocity of content update's, content precious, timestamps,predicted time availability, required content acquisition triggers,content acquisition trigger detection indicators, and a duplicativeindicator with a pending content request. For example, the contentacquisition module 180 receives the acquisition timing information 200from the acquisition timing module 188, where the acquisition timingmodule 188 generates the acquisition timing information 200 based on anacquisition timing information request from the content acquisitionmodule 180 and where the content acquisition module 180 generates theacquisition timing information request based on the collections request132.

Having obtained the content selection information 194, the sourceselection information 196, and the acquisition timing information 200,the content acquisition module 180 issues a content request 126 to acontent source utilizing security information 198 from the contentsecurity module 186, where the content acquisition module 180 generatesthe content request 126 in accordance with the content selectioninformation 194, the source selection information 196, and theacquisition timing information 200. The security information 198includes one or more of source priority requirements, requester securityinformation, available security procedures, and security credentials fortrust and/or encryption. For example, the content acquisition module 180generates the content request 126 to request a particular content typein accordance with the content selection information 194 and to includesecurity parameters of the security information 198, initiates sendingof the content request 126 in accordance with the acquisition timinginformation 200, and sends the content request 126 to a particulartargeted content source in accordance with the source selectioninformation 196.

In response to receiving a content response 128, the content acquisitionmodule 180 determines the quality level of received content extractedfrom the content response 128. For example, the content acquisitionmodule 180 receives content quality information 204 from the contentquality module 192, where the content quality module 192 generates thequality level of the received content based on receiving a contentquality request from the content acquisition module 180 and where thecontent acquisition module 180 generates the content quality requestbased on content extracted from the content response 128. The contentquality information includes one or more of a content reliabilitythreshold range, a content accuracy threshold range, a desired contentquality level, a predicted content quality level, and a predicted levelof trust.

When the quality level is below a minimum desired quality thresholdlevel, the content acquisition module 180 facilitates acquisition offurther content. The facilitating includes issuing another contentrequest 126 to a same content source and/or to another content source toreceive and interpret further received content. When the quality levelis above the minimum desired quality threshold level, the contentacquisition module 180 issues a collections response 134 to therequester. The issuing includes processing the content in accordancewith a transformation approach to produce transformed content,generating the collections response 134 to include the transformedcontent, and sending the collections response 134 to the requester. Theprocessing of the content to produce the transformed content includesreceiving content transformation information 202 from the contenttransformation module 190, where the content transformation module 190transforms the content in accordance with the transformation approach toproduce the transformed content. The content transformation informationincludes a desired format, available formats, recommended formatting,the received content, transformation instructions, and the transformedcontent.

FIG. 5B is a logic diagram of an embodiment of a method for obtainingcontent within a computing system. In particular, a method is presentedfor use in conjunction with one or more functions and features describedin conjunction with FIGS. 1-3, 4A-4C, 5A, and also FIG. 5B. The methodincludes step 210 where a processing module of one or more processingmodules of one or more computing devices of the computing systemreceives a collections request from the requester. The method continuesat step 212 where the processing module determines content selectioninformation. The determining includes interpreting the collectionsrequest to identify requirements of the content.

The method continues at step 214 where the processing module determinessource selection information. The determining includes interpreting thecollections request to identify and select one or more sources for thecontent to be collected. The method continues at step 216 where theprocessing module determines acquisition timing information. Thedetermining includes interpreting the collections request to identifytiming requirements for the acquisition of the content from the one ormore sources. The method continues at step 218 where the processingmodule issues a content request utilizing security information and inaccordance with one or more of the content selection information, thesource selection information, and the acquisition timing information.For example, the processing module issues the content request to the oneor more sources for the content in accordance with the contentrequirements, where the sending of the request is in accordance with theacquisition timing information.

The method continues at step 220 where the processing module determinesa content quality level for received content area the determiningincludes receiving the content from the one or more sources, obtainingcontent quality information for the received content based on a qualityanalysis of the received content. The method branches to step 224 whenthe content quality level is favorable and the method continues to step222 when the quality level is unfavorable. For example, the processingmodule determines that the content quality level is favorable when thecontent quality level is equal to or above a minimum quality thresholdlevel and determines that the content quality level is unfavorable whenthe content quality level is less than the minimum quality thresholdlevel.

When the content quality level is unfavorable, the method continues atstep 222 where the processing module facilitates acquisition and furthercontent. For example, the processing module issues further contentrequests and receives further content for analysis. When the contentquality level is favorable, the method continues at step 224 where theprocessing module issues a collections response to the requester. Theissuing includes generating the collections response and sending thecollections response to the requester. The generating of the collectionsresponse may include transforming the received content into transformedcontent in accordance with a transformation approach (e.g.,reformatting, interpreting absolute meaning and translating into anotherlanguage in accordance with the absolute meaning, etc.).

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIG. 5C is a schematic block diagram of an embodiment of the querymodule 124 of FIG. 4A that includes an answer acquisition module 230, acontent requirements module 232 a source requirements module 234, acontent security module 236, an answer timing module 238, an answertransformation module 240, and an answer quality module 242. Generally,an embodiment of this invention presents solutions where the querymodule 124 supports responding to a query.

In an example of operation of the responding to the query, the answeracquisition module 230 receives a query request 136 from a requester.The answer acquisition module 230 obtains content requirementsinformation 248 based on the query request 136. The content requirementsinformation 248 includes one or more of content parameters, a desiredcontent type, a desired content source if any, a content type if any,candidate source identifiers, a content profile, and a question of thequery request 136. For example, the answer acquisition module 230receives the content requirements information 248 from the contentrequirements module 232, where the content requirements module 232generates the content requirements information 248 based on a contentrequirements information request from the answer acquisition module 230and where the answer acquisition module 230 generates the contentrequirements information request based on the query request 136.

The answer acquisition module 230 obtains source requirementsinformation 250 based on the query request 136. The source requirementsinformation 250 includes one or more of candidate source identifiers, acontent profile, a desired source parameter, recommended sourceparameters, source priority levels, and recommended source accesstiming. For example, the answer acquisition module 230 receives thesource requirements information 250 from the source requirements module234, where the source requirements module 234 generates the sourcerequirements information 250 based on a source requirements informationrequest from the answer acquisition module 230 and where the answeracquisition module 230 generates the source requirements informationrequest based on the query request 136.

The answer acquisition module 230 obtains answer timing information 254based on the query request 136. The answer timing information 254includes one or more of requested answer timing, confirmed answertiming, source access testing results, estimated velocity of contentupdates, content freshness, timestamps, predicted time available,requested content acquisition trigger, and a content acquisition triggerdetected indicator. For example, the answer acquisition module 230receives the answer timing information 254 from the answer timing module238, where the answer timing module 238 generates the answer timinginformation 254 based on an answer timing information request from theanswer acquisition module 230 and where the answer acquisition module230 generates the answer timing information request based on the queryrequest 136.

Having obtained the content requirements information 248, the sourcerequirements information 250, and the answer timing information 254, theanswer acquisition module 230 determines whether to issue an IEI request244 and/or a collections request 132 based on one or more of the contentrequirements information 248, the source requirements information 250,and the answer timing information 254. For example, the answeracquisition module 230 selects the IEI request 244 when an immediateanswer to a simple query request 136 is required and is expected to havea favorable quality level. As another example, the answer acquisitionmodule 230 selects the collections request 132 when a longer-term answeris required as indicated by the answer timing information to beforeand/or when the query request 136 has an unfavorable quality level.

When issuing the IEI request 244, the answer acquisition module 230generates the IEI request 244 in accordance with security information252 received from the content security module 236 and based on one ormore of the content requirements information 248, the sourcerequirements information 250, and the answer timing information 254.Having generated the IEI request 244, the answer acquisition module 230sends the IEI request 244 to at least one IEI module.

When issuing the collections request 132, the answer acquisition module230 generates the collections request 132 in accordance with thesecurity information 252 received from the content security module 236and based on one or more of the content requirements information 248,the source requirements information 250, and the answer timinginformation 254. Having generated the collections request 132, theanswer acquisition module 230 sends the collections request 132 to atleast one collections module. Alternatively, the answer acquisitionmodule 230 facilitate sending of the collections request 132 to one ormore various user devices (e.g., to access a subject matter expert).

The answer acquisition module 230 determines a quality level of areceived answer extracted from a collections response 134 and/or an IEIresponse 246. For example, the answer acquisition module 230 extractsthe quality level of the received answer from answer quality information258 received from the answer quality module 242 in response to an answerquality request from the answer acquisition module 230. When the qualitylevel is unfavorable, the answer acquisition module 230 facilitatesobtaining a further answer. The facilitation includes issuing at leastone of a further IEI request 244 and a further collections request 132to generate a further answer for further quality testing. When thequality level is favorable, the answer acquisition module 230 issues aquery response 140 to the requester. The issuing includes generating thequery response 140 based on answer transformation information 256received from the answer transformation module 240, where the answertransformation module 240 generates the answer transformationinformation 256 to include a transformed answer based on receiving theanswer from the answer acquisition module 230. The answer transformationinformation 250 6A further include the question, a desired format of theanswer, available formats, recommended formatting, received IEIresponses, transformation instructions, and transformed IEI responsesinto an answer.

FIG. 5D is a logic diagram of an embodiment of a method for providing aresponse to a query within a computing system. In particular, a methodis presented for use in conjunction with one or more functions andfeatures described in conjunction with FIGS. 1-3, 4A-4C, 5C, and alsoFIG. 5D. The method includes step 270 where a processing module of oneor more processing modules of one or more computing devices of thecomputing system receives a query request (e.g., a question) from arequester. The method continues at step 272 where the processing moduledetermines content requirements information. The determining includesinterpreting the query request to produce the content requirements. Themethod continues at step 274 where the processing module determinessource requirements information. The determining includes interpretingthe query request to produce the source requirements. The methodcontinues at step 276 where the processing module determines answertiming information. The determining includes interpreting the queryrequest to produce the answer timing information.

The method continues at step 278 the processing module determineswhether to issue an IEI request and/or a collections request. Forexample, the determining includes selecting the IEI request when theanswer timing information indicates that a simple one-time answer isappropriate. As another example, the processing module selects thecollections request when the answer timing information indicates thatthe answer is associated with a series of events over an event timeframe.

When issuing the IEI request, the method continues at step 280 where theprocessing module issues the IEI request to an IEI module. The issuingincludes generating the IEI request in accordance with securityinformation and based on one or more of the content requirementsinformation, the source requirements information, and the answer timinginformation.

When issuing the collections request, the method continues at step 282where the processing module issues the collections request to acollections module. The issuing includes generating the collectionsrequest in accordance with the security information and based on one ormore of the content requirements information, the source requirementsinformation, and the answer timing information. Alternatively, theprocessing module issues both the IEI request and the collectionsrequest when a satisfactory partial answer may be provided based on acorresponding IEI response and a further more generalized and specificanswer may be provided based on a corresponding collections response andassociated further IEI response.

The method continues at step 284 where the processing module determinesa quality level of a received answer. The determining includesextracting the answer from the collections response and/or the IEIresponse and interpreting the answer in accordance with one or more ofthe content requirements information, the source requirementsinformation, the answer timing information, and the query request toproduce the quality level. The method branches to step 288 when thequality level is favorable and the method continues to step 286 when thequality level is unfavorable. For example, the processing moduleindicates that the quality level is favorable when the quality level isequal to or greater than a minimum answer quality threshold level. Asanother example, the processing module indicates that the quality levelis unfavorable when the quality level is less than the minimum answerquality threshold level.

When the quality level is unfavorable, the method continues at step 286where the processing module obtains a further answer. The obtainingincludes at least one of issuing a further IEI request and a furthercollections request to facilitate obtaining of a further answer forfurther answer quality level testing as the method loops back to step270. When the quality level is favorable, the method continues at step288 where the processing module issues a query response to therequester. The issuing includes transforming the answer into atransformed answer in accordance with an answer transformation approach(e.g., formatting, further interpretations of the virtual question inlight of the answer and further knowledge) and sending the transformedanswer to the requester as the query response.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIG. 5E is a schematic block diagram of an embodiment of the identigenentigen intelligence (IEI) module 122 of FIG. 4A that includes a contentingestion module 300, an element identification module 302, andinterpretation module 304, and answer resolution module 306, and an IEIcontrol module 308. Generally, an embodiment of this invention presentssolutions where the IEI module 122 supports interpreting content toproduce knowledge that may be utilized to answer questions.

In an example of operation of the producing and utilizing of theknowledge, the content ingestion module 300 generates formatted content314 based on question content 312 and/or source content 310, where theIEI module 122 receives an IEI request 244 that includes the questioncontent 312 and the IEI module 122 receives a collections response 134that includes the source content 310. The source content 310 includescontent from a source extracted from the collections response 134. Thequestion content 312 includes content extracted from the IEI request 244(e.g., content paired with a question). The content ingestion module 300generates the formatted content 314 in accordance with a formattingapproach (e.g., creating proper sentences from words of the content).The formatted content 314 includes modified content that is compatiblewith subsequent element identification (e.g., complete sentences,combinations of words and interpreted sounds and/or inflection cues withtemporal associations of words).

The element identification module 302 processes the formatted content314 based on element rules 318 and an element list 332 to produceidentified element information 340. Rules 316 includes the element rules318 (e.g., match, partial match, language translation, etc.). Lists 330includes the element list 332 (e.g., element ID, element context ID,element usage ID, words, characters, symbols etc.). The IEI controlmodule 308 may provide the rules 316 and the lists 330 by accessingstored data 360 from a memory associated with the IEI module 122.Generally, an embodiment of this invention presents solutions where thestored data 360 may further include one or more of a descriptivedictionary, categories, representations of element sets, element list,sequence data, pending questions, pending request, recognized elements,unrecognized elements, errors, etc.

The identified element information 340 includes one or more ofidentifiers of elements identified in the formatted content 314, mayinclude ordering and/or sequencing and grouping information. Forexample, the element identification module 302 compares elements of theformatted content 314 to known elements of the element list 332 toproduce identifiers of the known elements as the identified elementinformation 340 in accordance with the element rules 318. Alternatively,the element identification module 302 outputs un-identified elementinformation 342 to the IEI control module 308, where the un-identifiedelement information 342 includes temporary identifiers for elements notidentifiable from the formatted content 314 when compared to the elementlist 332.

The interpretation module 304 processes the identified elementinformation 340 in accordance with interpretation rules 320 (e.g.,potentially valid permutations of various combinations of identifiedelements), question information 346 (e.g., a question extracted from theIEI request 244 which may be paired with content associated with thequestion), and a groupings list 334 (e.g., representations of associatedgroups of representations of things, a set of element identifiers, validelement usage IDs in accordance with similar, an element context,permutations of sets of identifiers for possible interpretations of asentence or other) to produce interpreted information 344. Theinterpreted information 344 includes potentially valid interpretationsof combinations of identified elements. Generally, an embodiment of thisinvention presents solutions where the interpretation module 304supports producing the interpreted information 344 by consideringpermutations of the identified element information 340 in accordancewith the interpretation rules 320 and the groupings list 334.

The answer resolution module 306 processes the interpreted information344 based on answer rules 322 (e.g., guidance to extract a desiredanswer), the question information 346, and inferred question information352 (e.g., posed by the IEI control module or analysis of generalcollections of content or refinement of a stated question from arequest) to produce preliminary answers 354 and an answer quality level356. The answer generally lies in the interpreted information 344 asboth new content received and knowledge based on groupings list 334generated based on previously received content. The preliminary answers354 includes an answer to a stated or inferred question that subjectfurther refinement. The answer quality level 356 includes adetermination of a quality level of the preliminary answers 354 based onthe answer rules 322. The inferred question information 352 may furtherbe associated with time information 348, where the time informationincludes one or more of current real-time, a time reference associatedwith entity submitting a request, and a time reference of a collectionsresponse.

When the IEI control module 308 determines that the answer quality level356 is below an answer quality threshold level, the IEI control module308 facilitates collecting of further content (e.g., by issuing acollections request 132 and receiving corresponding collectionsresponses 134 for analysis). When the answer quality level 356 comparesfavorably to the answer quality threshold level, the IEI control module308 issues an IEI response 246 based on the preliminary answers 354.When receiving training information 358, the IEI control module 308facilitates updating of one or more of the lists 330 and the rules 316and stores the updated list 330 and the updated rules 316 in thememories as updated stored data 360.

FIG. 5F is a logic diagram of an embodiment of a method for analyzingcontent within a computing system. In particular, a method is presentedfor use in conjunction with one or more functions and features describedin conjunction with FIGS. 1-3, 4A-4C, 5E, and also FIG. 5F. The methodincludes step 370 where a processing module of one or more processingmodules of one or more computing devices of the computing systemfacilitates updating of one or more rules and lists based on one or moreof received training information and received content. For example, theprocessing module updates rules with received rules to produce updatedrules and updates element lists with received elements to produceupdated element lists. As another example, the processing moduleinterprets the received content to identify a new word for at leasttemporary inclusion in the updated element list.

The method continues at step 372 where the processing module transformsat least some of the received content into formatted content. Forexample, the processing module processes the received content inaccordance with a transformation approach to produce the formattedcontent, where the formatted content supports compatibility withsubsequent element identification (e.g., typical sentence structures ofgroups of words).

The method continues at step 374 where the processing module processesthe formatted content based on the rules and the lists to produceidentified element information and/or an identified element information.For example, the processing module compares the formatted content toelement lists to identify a match producing identifiers for identifiedelements or new identifiers for unidentified elements when there is nomatch.

The method continues at step 376 with a processing module processes theidentified element information based on rules, the lists, and questioninformation to produce interpreted information. For example, theprocessing module compares the identified element information toassociated groups of representations of things to generate potentiallyvalid interpretations of combinations of identified elements.

The method continues at step 378 where the processing module processesthe interpreted information based on the rules, the questioninformation, and inferred question information to produce preliminaryanswers. For example, the processing module matches the interpretedinformation to one or more answers (e.g., embedded knowledge based on afact base built from previously received content) with highestcorrectness likelihood levels that is subject to further refinement.

The method continues at step 380 where the processing module generatesan answer quality level based on the preliminary answers, the rules, andthe inferred question information. For example, the processing modulepredicts the answer correctness likelihood level based on the rules, theinferred question information, and the question information. The methodbranches to step 384 when the answer quality level is favorable and themethod continues to step 382 when the answer quality level isunfavorable. For example, the generating of the answer quality levelfurther includes the processing module indicating that the answerquality level is favorable when the answer quality level is greater thanor equal to a minimum answer quality threshold level. As anotherexample, the generating of the answer quality level further includes theprocessing module indicating that the answer quality level isunfavorable when the answer quality level is less than the minimumanswer quality threshold level.

When the answer quality level is unfavorable, the method continues atstep 382 where the processing module facilitates gathering clarifyinginformation. For example, the processing module issues a collectionsrequest to facilitate receiving further content and or request questionclarification from a question requester. When the answer quality levelis favorable, the method continues at step 384 where the processingmodule issues a response that includes one or more answers based on thepreliminary answers and/or further updated preliminary answers based ongathering further content. For example, the processing module generatesa response that includes one or more answers and the answer qualitylevel and issues the response to the requester.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIG. 6A is a schematic block diagram of an embodiment of the elementidentification module 302 of FIG. 5A and the interpretation module 304of FIG. 5A. The element identification module 302 includes an elementmatching module 400 and an element grouping module 402. Theinterpretation module 304 includes a grouping matching module 404 and agrouping interpretation module 406. Generally, an embodiment of thisinvention presents solutions where the element identification module 302supports identifying potentially valid permutations of groupings ofelements while the interpretation module 304 interprets the potentiallyvalid permutations of groupings of elements to produce interpretedinformation that includes the most likely of groupings based on aquestion.

In an example of operation of the identifying of the potentially validpermutations of groupings of elements, when matching elements of theformatted content 314, the element matching module 400 generates matchedelements 412 (e.g., identifiers of elements contained in the formattedcontent 314) based on the element list 332. For example, the elementmatching module 400 matches a received element to an element of theelement list 332 and outputs the matched elements 412 to include anidentifier of the matched element. When finding elements that areunidentified, the element matching module 400 outputs un-recognizedwords information 408 (e.g., words not in the element list 332, maytemporarily add) as part of un-identified element information 342. Forexample, the element matching module 400 indicates that a match cannotbe made between a received element of the formatted content 314,generates the unrecognized words info 408 to include the receivedelement and/or a temporary identifier, and issues and updated elementlist 414 that includes the temporary identifier and the correspondingunidentified received element.

The element grouping module 402 analyzes the matched elements 412 inaccordance with element rules 318 to produce grouping error information410 (e.g., incorrect sentence structure indicators) when a structuralerror is detected. The element grouping module 402 produces identifiedelement information 340 when favorable structure is associated with thematched elements in accordance with the element rules 318. Theidentified element information 340 may further include groupinginformation of the plurality of permutations of groups of elements(e.g., several possible interpretations), where the grouping informationincludes one or more groups of words forming an associated set and/orsuper-group set of two or more subsets when subsets share a common coreelement.

In an example of operation of the interpreting of the potentially validpermutations of groupings of elements to produce the interpretedinformation, the grouping matching module 404 analyzes the identifiedelement information 340 in accordance with a groupings list 334 toproduce validated groupings information 416. For example, the groupingmatching module 404 compares a grouping aspect of the identified elementinformation 340 (e.g., for each permutation of groups of elements ofpossible interpretations), generates the validated groupings information416 to include identification of valid permutations aligned with thegroupings list 334. Alternatively, or in addition to, the groupingmatching module 404 generates an updated groupings list 418 whendetermining a new valid grouping (e.g., has favorable structure andinterpreted meaning) that is to be added to the groupings list 334.

The grouping interpretation module 406 interprets the validatedgroupings information 416 based on the question information 346 and inaccordance with the interpretation rules 320 to produce interpretedinformation 344 (e.g., most likely interpretations, next most likelyinterpretations, etc.). For example, the grouping interpretation module406 obtains context, obtains favorable historical interpretations,processes the validated groupings based on interpretation rules 320,where each interpretation is associated with a correctness likelihoodlevel.

FIG. 6B is a logic diagram of an embodiment of a method for interpretinginformation within a computing system. In particular, a method ispresented for use in conjunction with one or more functions and featuresdescribed in conjunction with FIGS. 1-3, 4A-4C, 5E-5F, 6A, and also FIG.6B. The method includes step 430 where a processing module of one ormore processing modules of one or more computing devices of thecomputing system analyzes formatted content. For example, the processingmodule attempt to match a received element of the formatted content toone or more elements of an elements list. When there is no match, themethod branches to step 434 and when there is a match, the methodcontinues to step 432. When there is a match, the method continues atstep 432 where the processing module outputs matched elements (e.g., toinclude the matched element and/or an identifier of the matchedelement). When there is no match, the method continues at step 434 wherethe processing module outputs unrecognized words (e.g., elements and/ora temporary identifier for the unmatched element).

The method continues at step 436 where the processing module analyzesmatched elements. For example, the processing module attempt to match adetected structure of the matched elements (e.g., chained elements as ina received sequence) to favorable structures in accordance with elementrules. The method branches to step 440 when the analysis is unfavorableand the method continues to step 438 when the analysis is favorable.When the analysis is favorable matching a detected structure to thefavorable structure of the element rules, the method continues at step438 where the processing module outputs identified element information(e.g., an identifier of the favorable structure, identifiers of each ofthe detected elements). When the analysis is unfavorable matching adetected structure to the favorable structure of the element rules, themethod continues at step 440 where the processing module outputsgrouping error information (e.g., a representation of the incorrectstructure, identifiers of the elements of the incorrect structure, atemporary new identifier of the incorrect structure).

The method continues at step 442 where the processing module analyzesthe identified element information to produce validated groupingsinformation. For example, the processing module compares a groupingaspect of the identified element information and generates the validatedgroupings information to include identification of valid permutationsthat align with the groupings list. Alternatively, or in addition to,the processing module generates an updated groupings list whendetermining a new valid grouping.

The method continues at step 444 where the processing module interpretsthe validated groupings information to produce interpreted information.For example, the processing module obtains one or more of context andhistorical interpretations and processes the validated groupings basedon interpretation rules to generate the interpreted information, whereeach interpretation is associated with a correctness likelihood level(e.g., a quality level).

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIG. 6C is a schematic block diagram of an embodiment of the answerresolution module 306 of FIG. 5A that includes an interim answer module460, and answer prioritization module 462, and a preliminary answerquality module 464. Generally, an embodiment of this invention presentssolutions where the answer resolution module 306 supports producing ananswer for interpreted information 344.

In an example of operation of the providing of the answer, the interimanswer module 460 analyzes the interpreted information 344 based onquestion information 346 and inferred question information 352 toproduce interim answers 466 (e.g., answers to stated and/or inferredquestions without regard to rules that is subject to furtherrefinement). The answer prioritization module 462 analyzes the interimanswers 466 based on answer rules 322 to produce preliminary answer 354.For example, the answer prioritization module 462 identifies allpossible answers from the interim answers 466 that conform to the answerrules 322.

The preliminary answer quality module 464 analyzes the preliminaryanswers 354 in accordance with the question information 346, theinferred question information 352, and the answer rules 322 to producean answer quality level 356. For example, for each of the preliminaryanswers 354, the preliminary answer quality module 464 may compare a fitof the preliminary answer 354 to a corresponding previous answer andquestion quality level, calculate the answer quality level 356 based ona level of conformance to the answer rules 322, calculate the answerquality level 356 based on alignment with the inferred questioninformation 352, and determine the answer quality level 356 based on aninterpreted correlation with the question information 346.

FIG. 6D is a logic diagram of an embodiment of a method for producing ananswer within a computing system. In particular, a method is presentedfor use in conjunction with one or more functions and features describedin conjunction with FIGS. 1-3, 4A-4C, 5E-5F, 6C, and also FIG. 6D. Themethod includes step 480 where a processing module of one or moreprocessing modules of one or more computing devices of the computingsystem analyzes received interpreted information based on questioninformation and inferred question information to produce one or moreinterim answers. For example, the processing module generates potentialanswers based on patterns consistent with previously produced knowledgeand likelihood of correctness.

The method continues at step 482 where the processing module analyzesthe one or more interim answers based on answer rules to producepreliminary answers. For example, the processing module identifies allpossible answers from the interim answers that conform to the answerrules. The method continues at step 484 where the processing moduleanalyzes the preliminary answers in accordance with the questioninformation, the inferred question information, and the answer rules toproduce an answer quality level. For example, for each of the elementaryanswers, the processing module may compare a fit of the preliminaryanswer to a corresponding previous answer-and-answer quality level,calculate the answer quality level based on performance to the answerrules, calculate answer quality level based on alignment with theinferred question information, and determine the answer quality levelbased on interpreted correlation with the question information.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIG. 7A is an information flow diagram for interpreting informationwithin a computing system, where sets of entigens 504 are interpretedfrom sets of identigens 502 which are interpreted from sentences ofwords 500. Such identigen entigen intelligence (IEI) processing of thewords (e.g., to IEI process) includes producing one or more of interimknowledge, a preliminary answer, and an answer quality level. Forexample, the IEI processing includes identifying permutations ofidentigens of a phrase of a sentence (e.g., interpreting humanexpressions to produce identigen groupings for each word of ingestedcontent), reducing the permutations of identigens (e.g., utilizing rulesto eliminate unfavorable permutations), mapping the reduced permutationsof identigens to at least one set of entigens (e.g., most likelyidentigens become the entigens) to produce the interim knowledge,processing the knowledge in accordance with a knowledge database (e.g.,comparing the set of entigens to the knowledge database) to produce apreliminary answer, and generating the answer quality level based on thepreliminary answer for a corresponding domain.

Human expressions are utilized to portray facts and fiction about thereal world. The real-world includes items, actions, and attributes. Thehuman expressions include textual words, textual symbols, images, andother sensorial information (e.g., sounds). It is known that many words,within a given language, can mean different things based on groupingsand orderings of the words. For example, the sentences of words 500 caninclude many different forms of sentences that mean vastly differentthings even when the words are very similar.

The present invention presents solutions where the computing system 10supports producing a computer-based representation of a truest meaningpossible of the human expressions given the way that multitudes of humanexpressions relate to these meanings. As a first step of the flowdiagram to transition from human representations of things to a mostprecise computer representation of the things, the computer identifiesthe words, phrases, sentences, etc. from the human expressions toproduce the sets of identigens 502. Each identigen includes anidentifier of their meaning and an identifier of an instance for eachpossible language, culture, etc. For example, the words car andautomobile share a common meaning identifier but have different instanceidentifiers since they are different words and are spelled differently.As another example, the word duck is associated both with a bird and anaction to elude even though they are spelled the same. In this examplethe bird duck has a different meaning than the elude duck and as sucheach has a different meaning identifier of the corresponding identigens.

As a second step of the flow diagram to transition from humanrepresentations of things to the most precise computer representation ofthe things, the computer extracts meaning from groupings of theidentified words, phrases, sentences, etc. to produce the sets ofentigens 504. Each entigen includes an identifier of a singleconceivable and perceivable thing in space and time (e.g., independentof language and other aspects of the human expressions). For example,the words car and automobile are different instances of the same meaningand point to a common shared entigen. As another example, the word duckfor the bird meaning has an associated unique entigen that is differentthan the entigen for the word duck for the elude meaning.

As a third step of the flow diagram to transition from human expressionsof things to the most precise computer representation of the things, thecomputer reasons facts from the extracted meanings. For example, thecomputer maintains a fact-based of the valid meanings from the validgroupings or sets of entigens so as to support subsequent inferences,deductions, rationalizations of posed questions to produce answers thatare aligned with a most factual view. As time goes on, and as an entigenhas been identified, it can encounter an experience transformations intime, space, attributes, actions, and words which are used to identifyit without creating contradictions or ever losing its identity.

FIG. 7B is a relationship block diagram illustrating an embodiment ofrelationships between things 510 and representations of things 512within a computing system. The things 510 includes conceivable andperceivable things including actions 522, items 524, and attributes 526.The representation of things 512 includes representations of things usedby humans 514 and representation of things used by of computing devices516 of embodiments of the present invention. The things 510 relates tothe representations of things used by humans 514 where the inventionpresents solutions where the computing system 10 supports mapping therepresentations of things used by humans 514 to the representations ofthings used by computing devices 516, where the representations ofthings used by computing devices 516 map back to the things 510.

The representations of things used by humans 514 includes textual words528, textual symbols 530, images (e.g., non-textual) 532, and othersensorial information 534 (e.g., sounds, sensor data, electrical fields,voice inflections, emotion representations, facial expressions,whistles, etc.). The representations of things used by computing devices516 includes identigens 518 and entigens 520. The representations ofthings used by humans 514 maps to the identigens 518 and the identigens518 map to the entigens 520. The entigens 520 uniquely maps back to thethings 510 in space and time, a truest meaning the computer is lookingfor to create knowledge and answer questions based on the knowledge.

To accommodate the mapping of the representations of things used byhumans 514 to the identigens 518, the identigens 518 is partitioned intoactenyms 544 (e.g., actions), itenyms 546 (e.g., items), attrenyms 548(e.g., attributes), and functionals 550 (e.g., that join and/ordescribe). Each of the actenyms 544, itenyms 546, and attrenyms 548 maybe further classified into singulatums 552 (e.g., identify one uniqueentigen) and pluratums 554 (e.g., identify a plurality of entigens thathave similarities).

Each identigen 518 is associated with an identigens identifier (IDN)536. The IDN 536 includes a meaning identifier (ID) 538 portion, aninstance ID 540 portion, and a type ID 542 portion. The meaning ID 538includes an identifier of common meaning. The instance ID 540 includesan identifier of a particular word and language. The type ID 542includes one or more identifiers for actenyms, itenyms, attrenyms,singulatums, pluratums, a time reference, and any other reference todescribe the IDN 536. The mapping of the representations of things usedby humans 514 to the identigens 518 by the computing system of thepresent invention includes determining the identigens 518 in accordancewith logic and instructions for forming groupings of words.

Generally, an embodiment of this invention presents solutions where theidentigens 518 map to the entigens 520. Multiple identigens may map to acommon unique entigen. The mapping of the identigens 518 to the entigens520 by the computing system of the present invention includesdetermining entigens in accordance with logic and instructions forforming groupings of identigens.

FIG. 7C is a diagram of an embodiment of a synonym words table 570within a computing system, where the synonym words table 570 includesmultiple fields including textual words 572, identigens 518, andentigens 520. The identigens 518 includes fields for the meaningidentifier (ID) 538 and the instance ID 540. The computing system of thepresent invention may utilize the synonym words table 570 to map textualwords 572 to identigens 518 and map the identigens 518 to entigens 520.For example, the words car, automobile, auto, bil (Swedish), carro(Spanish), and bil (Danish) all share a common meaning but are differentinstances (e.g., different words and languages). The words map to acommon meaning ID but to individual unique instant identifiers. Each ofthe different identigens map to a common entigen since they describe thesame thing.

FIG. 7D is a diagram of an embodiment of a polysemous words table 576within a computing system, where the polysemous words table 576 includesmultiple fields including textual words 572, identigens 518, andentigens 520. The identigens 518 includes fields for the meaningidentifier (ID) 538 and the instance ID 540. The computing system of thepresent invention may utilize the polysemous words table 576 to maptextual words 572 to identigens 518 and map the identigens 518 toentigens 520. For example, the word duck maps to four differentidentigens since the word duck has four associated different meanings(e.g., bird, fabric, to submerge, to elude) and instances. Each of theidentigens represent different things and hence map to four differententigens.

FIG. 7E is a diagram of an embodiment of transforming words intogroupings within a computing system that includes a words table 580, agroupings of words section to validate permutations of groupings, and agroupings table 584 to capture the valid groupings. The words table 580includes multiple fields including textual words 572, identigens 518,and entigens 520. The identigens 518 includes fields for the meaningidentifier (ID) 538, the instance ID 540, and the type ID 542. Thecomputing system of the present invention may utilize the words table580 to map textual words 572 to identigens 518 and map the identigens518 to entigens 520. For example, the word pilot may refer to a flyerand the action to fly. Each meaning has a different identigen anddifferent entigen.

The computing system the present invention may apply rules to the fieldsof the words table 580 to validate various groupings of words. Thosethat are invalid are denoted with a “X” while those that are valid areassociated with a check mark. For example, the grouping “pilot Tom” isinvalid when the word pilot refers to flying and Tom refers to a person.The identigen combinations for the flying pilot and the person Tom aredenoted as invalid by the rules. As another example, the grouping “pilotTom” is valid when the word pilot refers to a flyer and Tom refers tothe person. The identigen combinations for the flyer pilot and theperson Tom are denoted as valid by the rules.

The groupings table 584 includes multiple fields including grouping ID586, word strings 588, identigens 518, and entigens 520. The computingsystem of the present invention may produce the groupings table 584 as astored fact base for valid and/or invalid groupings of words identifiedby their corresponding identigens. For example, the valid grouping“pilot Tom” referring to flyer Tom the person is represented with agrouping identifier of 3001 and identity and identifiers 150.001 and457.001. The entigen field 520 may indicate associated entigens thatcorrespond to the identigens. For example, entigen e717 corresponds tothe flyer pilot meaning and entigen e61 corresponds to the time theperson meaning. Alternatively, or in addition to, the entigen field 520may be populated with a single entigen identifier (ENI).

The word strings field 588 may include any number of words in a string.Different ordering of the same words can produce multiple differentstrings and even different meanings and hence entigens. More broadly,each entry (e.g., role) of the groupings table 584 may refer togroupings of words, two or more word strings, an idiom, just identigens,just entigens, and/or any combination of the preceding elements. Eachentry has a unique grouping identifier. An idiom may have a uniquegrouping ID and include identifiers of original word identigens andreplacing identigens associated with the meaning of the idiom not justthe meaning of the original words. Valid groupings may still haveambiguity on their own and may need more strings and/or context toselect a best fit when interpreting a truest meaning of the grouping.

FIG. 8A is a data flow diagram for accumulating knowledge within acomputing system, where a computing device, at a time=t0, ingests andprocesses facts 598 at a step 590 based on rules 316 and fact baseinformation 600 to produce groupings 602 for storage in a fact base 592(e.g., words, phrases, word groupings, identigens, entigens, qualitylevels). The facts 598 may include information from books, archive data,Central intelligence agency (CIA) world fact book, trusted content, etc.The ingesting may include filtering to organize and promote better validgroupings detection (e.g., considering similar domains together). Thegroupings 602 includes one or more of groupings identifiers, identigenidentifiers, entigen identifiers, and estimated fit quality levels. Theprocessing step 590 may include identifying identigens from words of thefacts 598 in accordance with the rules 316 and the fact base info 600and identifying groupings utilizing identigens in accordance with rules316 and fact base info 600.

Subsequent to ingestion and processing of the facts 598 to establish thefact base 592, at a time=t1+, the computing device ingests and processesnew content 604 at a step 594 in accordance with the rules 316 and thefact base information 600 to produce preliminary grouping 606. The newcontent may include updated content (e.g., timewise) from periodicals,newsfeeds, social media, etc. The preliminary grouping 606 includes oneor more of preliminary groupings identifiers, preliminary identigenidentifiers, preliminary entigen identifiers, estimated fit qualitylevels, and representations of unidentified words.

The computing device validates the preliminary groupings 606 at a step596 based on the rules 316 and the fact base info 600 to produce updatedfact base info 608 for storage in the fact base 592. The validatingincludes one or more of reasoning a fit of existing fact base info 600with the new preliminary grouping 606, discarding preliminary groupings,updating just time frame information associated with an entry of theexisting fact base info 600 (e.g., to validate knowledge for thepresent), creating new entigens, and creating a median entigen tosummarize portions of knowledge within a median indicator as a qualitylevel indicator (e.g., suggestive not certain).

Storage of the updated fact base information 608 captures patterns thatdevelop by themselves instead of searching for patterns as in prior artartificial intelligence systems. Growth of the fact base 592 enablessubsequent reasoning to create new knowledge including deduction,induction, inference, and inferential sentiment (e.g., a chain ofsentiment sentences). Examples of sentiments includes emotion, beliefs,convictions, feelings, judgments, notions, opinions, and views.

FIG. 8B is a diagram of an embodiment of a groupings table 620 within acomputing system. The groupings table 620 includes multiple fieldsincluding grouping ID 586, word strings 588, an IF string 622 and a THENstring 624. Each of the fields for the IF string 622 and the THEN string624 includes fields for an identigen (IDN) string 626, and an entigen(ENI) string 628. The computing system of the present invention mayproduce the groupings table 620 as a stored fact base to enable IF THENbased inference to generate a new knowledge inference 630.

As a specific example, grouping 5493 points out the logic of IF someonehas a tumor, THEN someone is sick and the grouping 5494 points of thelogic that IF someone is sick, THEN someone is sad. As a result ofutilizing inference, the new knowledge inference 630 may producegrouping 5495 where IF someone has a tumor, THEN someone is possibly sad(e.g., or is sad). FIG. 8C is a data flow diagram for answeringquestions utilizing accumulated knowledge within a computing system,where a computing device ingests and processes question information 346at a step 640 based on rules 316 and fact base info 600 from a fact base592 to produce preliminary grouping 606. The ingesting and processingquestions step 640 includes identifying identigens from words of aquestion in accordance with the rules 316 and the fact base information600 and may also include identifying groupings from the identifiedidentigens in accordance with the rules 316 and the fact baseinformation 600.

The computing device validates the preliminary grouping 606 at a step596 based on the rules 316 and the fact base information 600 to produceidentified element information 340. For example, the computing devicereasons fit of existing fact base information with new preliminarygroupings 606 to produce the identified element information 340associated with highest quality levels. The computing device interpretsa question of the identified element information 340 at a step 642 basedon the rules 316 and the fact base information 600. The interpreting ofthe question may include separating new content from the question andreducing the question based on the fact base information 600 and the newcontent.

The computing device produces preliminary answers 354 from theinterpreted information 344 at a resolve answer step 644 based on therules 316 and the fact base information 600. For example, the computingdevice compares the interpreted information 344 two the fact baseinformation 600 to produce the preliminary answers 354 with highestquality levels utilizing one or more of deduction, induction,inferencing, and applying inferential sentiments logic. Alternatively,or in addition to, the computing device may save new knowledgeidentified from the question information 346 to update the fact base592.

FIG. 8D is a data flow diagram for answering questions utilizinginterference within a computing system that includes a groupings table648 and the resolve answer step 644 of FIG. 8C. The groupings table 648includes multiple fields including fields for a grouping (GRP)identifier (ID) 586, word strings 588, an identigen (IDN) string 626,and an entigen (ENI) 628. The groupings table 648 may be utilized tobuild a fact base to enable resolving a future question into an answer.For example, the grouping 8356 notes knowledge that Michael sleeps eighthours and grouping 8357 notes that Michael usually starts to sleep at 11PM.

In a first question example that includes a question “Michaelsleeping?”, the resolve answer step 644 analyzes the question from theinterpreted information 344 in accordance with the fact base information600, the rules 316, and a real-time indicator that the current time is 1AM to produce a preliminary answer of “possibly YES” when inferring thatMichael is probably sleeping at 1 AM when Michael usually startssleeping at 11 PM and Michael usually sleeps for a duration of eighthours.

In a second question example that includes the question “Michaelsleeping?”, the resolve answer step 644 analyzes the question from theinterpreted information 344 in accordance with the fact base information600, the rules 316, and a real-time indicator that the current time isnow 11 AM to produce a preliminary answer of “possibly NO” wheninferring that Michael is probably not sleeping at 11 AM when Michaelusually starts sleeping at 11 PM and Michael usually sleeps for aduration of eight hours.

FIG. 8E is a relationship block diagram illustrating another embodimentof relationships between things and representations of things within acomputing system. While things in the real world are described withwords, it is often the case that a particular word has multiple meaningsin isolation. Interpreting the meaning of the particular word may hingeon analyzing how the word is utilized in a phrase, a sentence, multiplesentences, paragraphs, and even whole documents or more. Describing andstratifying the use of words, word types, and possible meanings help ininterpreting a true meaning.

Humans utilize textual words 528 to represent things in the real world.Quite often a particular word has multiple instances of differentgrammatical use when part of a phrase of one or more sentences. Thegrammatical use 649 of words includes the nouns and the verbs, and alsoincludes adverbs, adjectives, pronouns, conjunctions, prepositions,determiners, exclamations, etc.

As an example of multiple grammatical use, the word “bat” in the Englishlanguage can be utilized as a noun or a verb. For instance, whenutilized as a noun, the word “bat” may apply to a baseball bat or mayapply to a flying “bat.” As another instance, when utilized as a verb,the word “bat” may apply to the action of hitting or batting an object,i.e., “bat the ball.”

To stratify word types by use, the words are associated with a word type(e.g., type identifier 542). The word types include objects (e.g., items524), characteristics (e.g., attributes 526), actions 522, and thefunctionals 550 for joining other words and describing words. Forexample, when the word “bat” is utilized as a noun, the word isdescribing the object of either the baseball bat or the flying bat. Asanother example, when the word “bat” is utilized as a verb, the word isdescribing the action of hitting.

To determine possible meanings, the words, by word type, are mapped toassociative meanings (e.g., identigens 518). For each possibleassociative meaning, the word type is documented with the meaning andfurther with an identifier (ID) of the instance (e.g., an identigenidentifier).

For the example of the word “bat” when utilized as a noun for thebaseball bat, a first identigen identifier 536-1 includes a type ID542-1 associated with the object 524, an instance ID 540-1 associatedwith the first identigen identifier (e.g., unique for the baseball bat),and a meaning ID 538-1 associated with the baseball bat. For the exampleof the word “bat” when utilized as a noun for the flying bat, a secondidentigen identifier 536-2 includes a type ID 542-1 associated with theobject 524, an instance ID 540-2 associated with the second identigenidentifier (e.g., unique for the flying bat), and a meaning ID 538-2associated with the flying bat. For the example of the word “bat” whenutilized as a verb for the bat that hits, a third identigen identifier536-2 includes a type ID 542-2 associated with the actions 522, aninstance ID 540-3 associated with the third identigen identifier (e.g.,unique for the bat that hits), and a meaning ID 538-3 associated withthe bat that hits.

With the word described by a type and possible associative meanings, acombination of full grammatical use of the word within the phrase etc.,application of rules, and utilization of an ever-growing knowledgedatabase that represents knowledge by linked entigens, the absolutemeaning (e.g., entigen 520) of the word is represented as a uniqueentigen. For example, a first entigen e1 represents the absolute meaningof a baseball bat (e.g., a generic baseball bat not a particularbaseball bat that belongs to anyone), a second entigen e2 represents theabsolute meaning of the flying bat (e.g., a generic flying bat not aparticular flying bat), and a third entigen e3 represents the absolutemeaning of the verb bat (e.g., to hit).

An embodiment of methods to ingest text to produce absolute meanings forstorage in a knowledge database are discussed in greater detail withreference to FIGS. 8F-H. Those embodiments further discuss thediscerning of the grammatical use, the use of the rules, and theutilization of the knowledge database to definitively interpret theabsolute meaning of a string of words.

Another embodiment of methods to respond to a query to produce an answerbased on knowledge stored in the knowledge database are discussed ingreater detail with reference to FIGS. 8J-L. Those embodiments furtherdiscuss the discerning of the grammatical use, the use of the rules, andthe utilization of the knowledge database to interpret the query. Thequery interpretation is utilized to extract the answer from theknowledge database to facilitate forming the query response.

FIGS. 8F and 8G are schematic block diagrams of another embodiment of acomputing system that includes the content ingestion module 300 of FIG.5E, the element identification module 302 of FIG. 5E, the interpretationmodule 304 of FIG. 5E, the IEI control module 308 of FIG. 5E, and the SSmemory 96 of FIG. 2. Generally, an embodiment of this invention providespresents solutions where the computing system 10 supports processingcontent to produce knowledge for storage in a knowledge database.

The processing of the content to produce the knowledge includes a seriesof steps. For example, a first step includes identifying words of aningested phrase to produce tokenized words. As depicted in FIG. 8F, aspecific example of the first step includes the content ingestion module300 comparing words of source content 310 to dictionary entries toproduce formatted content 314 that includes identifiers of known words.Alternatively, when a comparison is unfavorable, the temporaryidentifier may be assigned to an unknown word. For instance, the contentingestion module 300 produces identifiers associated with the words“the”, “black”, “bat”, “eats”, and “fruit” when the ingested phraseincludes “The black bat eats fruit”, and generates the formatted content314 to include the identifiers of the words.

A second step of the processing of the content to produce the knowledgeincludes, for each tokenized word, identifying one or more identigensthat correspond the tokenized word, where each identigen describes oneof an object, a characteristic, and an action. As depicted in FIG. 8F, aspecific example of the second step includes the element identificationmodule 302 performing a look up of identigen identifiers, utilizing anelement list 332 and in accordance with element rules 318, of the one ormore identigens associated with each tokenized word of the formattedcontent 314 to produce identified element information 340.

A unique identifier is associated with each of the potential object, thecharacteristic, and the action (OCA) associated with the tokenized word(e.g. sequential identigens). For instance, the element identificationmodule 302 identifies a functional symbol for “the”, identifies a singleidentigen for “black”, identifies two identigens for “bat” (e.g.,baseball bat and flying bat), identifies a single identigen for “eats”,and identifies a single identigen for “fruit.” When at least onetokenized word is associated with multiple identigens, two or morepermutations of sequential combinations of identigens for each tokenizedword result. For example, when “bat” is associated with two identigens,two permutations of sequential combinations of identigens result for theingested phrase.

A third step of the processing of the content to produce the knowledgeincludes, for each permutation of sequential combinations of identigens,generating a corresponding equation package (i.e., candidateinterpretation), where the equation package includes a sequentiallinking of pairs of identigens (e.g., relationships), where eachsequential linking pairs a preceding identigen to a next identigen, andwhere an equation element describes a relationship between pairedidentigens (OCAs) such as describes, acts on, is a, belongs to, did, didto, etc. Multiple OCAs occur for a common word when the word hasmultiple potential meanings (e.g., a baseball bat, a flying bat).

As depicted in FIG. 8F, a specific example of the third step includesthe interpretation module 304, for each permutation of identigens ofeach tokenized word of the identified element information 340, theinterpretation module 304 generates, in accordance with interpretationrules 320 and a groupings list 334, an equation package to include oneor more of the identifiers of the tokenized words, a list of identifiersof the identigens of the equation package, a list of pairing identifiersfor sequential pairs of identigens, and a quality metric associated witheach sequential pair of identigens (e.g., likelihood of a properinterpretation). For instance, the interpretation module 304 produces afirst equation package that includes a first identigen pairing of ablack bat (e.g., flying bat with a higher quality metric level), thesecond pairing of bat eats (e.g., the flying bat eats, with a higherquality metric level), and a third pairing of eats fruit, and theinterpretation module 304 produces a second equation package thatincludes a first pairing of a black bat (e.g., baseball bat, with aneutral quality metric level), the second pairing of bat eats (e.g., thebaseball bat eats, with a lower quality metric level), and a thirdpairing of eats fruit.

A fourth step of the processing of the content to produce the knowledgeincludes selecting a surviving equation package associated with a mostfavorable confidence level. As depicted in FIG. 8F, a specific exampleof the fourth step includes the interpretation module 304 applyinginterpretation rules 320 (i.e., inference, pragmatic engine, utilizingthe identifiers of the identigens to match against known validcombinations of identifiers of entigens) to reduce a number ofpermutations of the sequential combinations of identigens to produceinterpreted information 344 that includes identification of at least oneequation package as a surviving interpretation SI (e.g., higher qualitymetric level).

Non-surviving equation packages are eliminated that compare unfavorablyto pairing rules and/or are associated with an unfavorable qualitymetric levels to produce a non-surviving interpretation NSI 2 (e.g.,lower quality metric level), where an overall quality metric level maybe assigned to each equation package based on quality metric levels ofeach pairing, such that a higher quality metric level of an equationpackage indicates a higher probability of a most favorableinterpretation. For instance, the interpretation module 304 eliminatesthe equation package that includes the second pairing indicating thatthe “baseball bat eats” which is inconsistent with a desired qualitymetric level of one or more of the groupings list 334 and theinterpretation rules 320 and selects the equation package associatedwith the “flying bat eats” which is favorably consistent with the one ormore of the quality metric levels of the groupings list 334 and theinterpretation rules 320.

A fifth step of the processing of the content to produce the knowledgeutilizing the confidence level includes integrating knowledge of thesurviving equation package into a knowledge database. For example,integrating at least a portion of the reduced OCA combinations into agraphical database to produce updated knowledge. As another example, theportion of the reduced OCA combinations may be translated into rows andcolumns entries when utilizing a rows and columns database rather than agraphical database. When utilizing the rows and columns approach for theknowledge database, subsequent access to the knowledge database mayutilize structured query language (SQL) queries.

As depicted in FIG. 8G, a specific example of the fifth step includesthe IEI control module 308 recovering fact base information 600 from SSmemory 96 to identify a portion of the knowledge database for potentialmodification utilizing the OCAs of the surviving interpretation SI 1(i.e., compare a pattern of relationships between the OCAs of thesurviving interpretation SI 1 from the interpreted information 344 torelationships of OCAs of the portion of the knowledge database includingpotentially new quality metric levels).

The fifth step further includes determining modifications (e.g.,additions, subtractions, further clarifications required wheninformation is complex, etc.) to the portion of the knowledge databasebased on the new quality metric levels. For instance, the IEI controlmodule 308 causes adding the element “black” as a “describes”relationship of an existing bat OCA and adding the element “fruit” as aneats “does to” relationship to implement the modifications to theportion of the fact base information 600 to produce updated fact baseinformation 608 for storage in the SS memory 96.

FIG. 8H is a logic diagram of an embodiment of a method for processingcontent to produce knowledge for storage within a computing system. Inparticular, a method is presented for use in conjunction with one ormore functions and features described in conjunction with FIGS. 1-8E,8F, and also FIG. 8G. The method includes step 650 where a processingmodule of one or more processing modules of one or more computingdevices of the computing system identifies words of an ingested phraseto produce tokenized words. The identified includes comparing words toknown words of dictionary entries to produce identifiers of known words.

For each tokenized word, the method continues at step 651 where theprocessing module identifies one or more identigens that corresponds tothe tokenized word, where each identigen describes one of an object, acharacteristic, and an action (e.g., OCA). The identifying includesperforming a lookup of identifiers of the one or more identigensassociated with each tokenized word, where the different identifiersassociated with each of the potential object, the characteristic, andthe action associated with the tokenized word.

The method continues at step 652 where the processing module, for eachpermutation of sequential combinations of identigens, generates aplurality of equation elements to form a corresponding equation package,where each equation element describes a relationship betweensequentially linked pairs of identigens, where each sequential linkingpairs a preceding identigen to a next identigen. For example, for eachpermutation of identigens of each tokenized word, the processing modulegenerates the equation package to include a plurality of equationelements, where each equation element describes the relationship (e.g.,describes, acts on, is a, belongs to, did, did too, etc.) betweensequentially adjacent identigens of a plurality of sequentialcombinations of identigens. Each equation element may be furtherassociated with a quality metric to evaluate a favorability level of aninterpretation in light of the sequence of identigens of the equationpackage.

The method continues at step 653 where the processing module selects asurviving equation package associated with most favorableinterpretation. For example, the processing module applies rules (i.e.,inference, pragmatic engine, utilizing the identifiers of the identigensto match against known valid combinations of identifiers of entigens),to reduce the number of permutations of the sequential combinations ofidentigens to identify at least one equation package, wherenon-surviving equation packages are eliminated the compare unfavorablyto pairing rules and/or are associated with an unfavorable qualitymetric levels to produce a non-surviving interpretation, where anoverall quality metric level is assigned to each equation package basedon quality metric levels of each pairing, such that a higher qualitymetric level indicates an equation package with a higher probability offavorability of correctness.

The method continues at step 654 where the processing module integratesknowledge of the surviving equation package into a knowledge database.For example, the processing module integrates at least a portion of thereduced OCA combinations into a graphical database to produce updatedknowledge. The integrating may include recovering fact base informationfrom storage of the knowledge database to identify a portion of theknowledge database for potential modifications utilizing the OCAs of thesurviving equation package (i.e., compare a pattern of relationshipsbetween the OCAs of the surviving equation package to relationships ofthe OCAs of the portion of the knowledge database including potentiallynew quality metric levels). The integrating further includes determiningmodifications (e.g., additions, subtractions, further clarificationsrequired when complex information is presented, etc.) to produce theupdated knowledge database that is based on fit of acceptable qualitymetric levels, and implementing the modifications to the portion of thefact base information to produce the updated fact base information forstorage in the portion of the knowledge database.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIGS. 8J and 8K are schematic block diagrams of another embodiment of acomputing system that includes the content ingestion module 300 of FIG.5E, the element identification module 302 of FIG. 5E, the interpretationmodule 304 of FIG. 5E, the answer resolution module 306 of FIG. 5E, andthe SS memory 96 of FIG. 2. Generally, an embodiment of this inventionprovides solutions where the computing system 10 supports for generatinga query response to a query utilizing a knowledge database.

The generating of the query response to the query includes a series ofsteps. For example, a first step includes identifying words of aningested query to produce tokenized words. As depicted in FIG. 8J, aspecific example of the first step includes the content ingestion module300 comparing words of query info 138 to dictionary entries to produceformatted content 314 that includes identifiers of known words. Forinstance, the content ingestion module 300 produces identifiers for eachword of the query “what black animal flies and eats fruit and insects?”

A second step of the generating of the query response to the queryincludes, for each tokenized word, identifying one or more identigensthat correspond the tokenized word, where each identigen describes oneof an object, a characteristic, and an action (OCA). As depicted in FIG.8J, a specific example of the second step includes the elementidentification module 302 performing a look up of identifiers, utilizingan element list 332 and in accordance with element rules 318, of the oneor more identigens associated with each tokenized word of the formattedcontent 314 to produce identified element information 340. A uniqueidentifier is associated with each of the potential object, thecharacteristic, and the action associated with a particular tokenizedword. For instance, the element identification module 302 produces asingle identigen identifier for each of the black color, an animal,flies, eats, fruit, and insects.

A third step of the generating of the query response to the queryincludes, for each permutation of sequential combinations of identigens,generating a corresponding equation package (i.e., candidateinterpretation). The equation package includes a sequential linking ofpairs of identigens, where each sequential linking pairs a precedingidentigen to a next identigen. An equation element describes arelationship between paired identigens (OCAs) such as describes, actson, is a, belongs to, did, did to, etc.

As depicted in FIG. 8J, a specific example of the third step includesthe interpretation module 304, for each permutation of identigens ofeach tokenized word of the identified element information 340,generating the equation packages in accordance with interpretation rules320 and a groupings list 334 to produce a series of equation elementsthat include pairings of identigens. For instance, the interpretationmodule 304 generates a first pairing to describe a black animal, asecond pairing to describe an animal that flies, a third pairing todescribe flies and eats, a fourth pairing to describe eats fruit, and afifth pairing to describe eats fruit and insects.

A fourth step of the generating the query response to the query includesselecting a surviving equation package associated with a most favorableinterpretation. As depicted in FIG. 8J, a specific example of the fourthstep includes the interpretation module 304 applying the interpretationrules 320 (i.e., inference, pragmatic engine, utilizing the identifiersof the identigens to match against known valid combinations ofidentifiers of entigens) to reduce the number of permutations of thesequential combinations of identigens to produce interpreted information344. The interpreted information 344 includes identification of at leastone equation package as a surviving interpretation SI 10, wherenon-surviving equation packages, if any, are eliminated that compareunfavorably to pairing rules to produce a non-surviving interpretation.

A fifth step of the generating the query response to the query includesutilizing a knowledge database, generating a query response to thesurviving equation package of the query, where the surviving equationpackage of the query is transformed to produce query knowledge forcomparison to a portion of the knowledge database. An answer isextracted from the portion of the knowledge database to produce thequery response.

As depicted in FIG. 8K, a specific example of the fifth step includesthe answer resolution module 306 interpreting the survivinginterpretation SI 10 of the interpreted information 344 in accordancewith answer rules 322 to produce query knowledge QK 10 (i.e., agraphical representation of knowledge when the knowledge databaseutilizes a graphical database). For example, the answer resolutionmodule 306 accesses fact base information 600 from the SS memory 96 toidentify the portion of the knowledge database associated with afavorable comparison of the query knowledge QK 10 (e.g., by comparingattributes of the query knowledge QK 10 to attributes of the fact baseinformation 600), and generates preliminary answers 354 that includesthe answer to the query. For instance, the answer is “bat” when theassociated OCAs of bat, such as black, eats fruit, eats insects, is ananimal, and flies, aligns with OCAs of the query knowledge.

FIG. 8L is a logic diagram of an embodiment of a method for generating aquery response to a query utilizing knowledge within a knowledgedatabase within a computing system. In particular, a method is presentedfor use in conjunction with one or more functions and features describedin conjunction with FIGS. 1-8D, 8J, and also FIG. 8K. The methodincludes step 655 where a processing module of one or more processingmodules of one or more computing devices of the computing systemidentifies words of an ingested query to produce tokenized words. Forexample, the processing module compares words to known words ofdictionary entries to produce identifiers of known words.

For each tokenized word, the method continues at step 656 where theprocessing module identifies one or more identigens that correspond tothe tokenized word, where each identigen describes one of an object, acharacteristic, and an action. For example, the processing moduleperforms a lookup of identifiers of the one or more identigensassociated with each tokenized word, where different identifiersassociated with each permutation of a potential object, characteristic,and action associated with the tokenized word.

For each permutation of sequential combinations of identigens, themethod continues at step 657 where the processing module generates aplurality of equation elements to form a corresponding equation package,where each equation element describes a relationship betweensequentially linked pairs of identigens. Each sequential linking pairs apreceding identigen to a next identigen. For example, for eachpermutation of identigens of each tokenized word, the processing moduleincludes all other permutations of all other tokenized words to generatethe equation packages. Each equation package includes a plurality ofequation elements describing the relationships between sequentiallyadjacent identigens of a plurality of sequential combinations ofidentigens.

The method continues at step 658 where the processing module selects asurviving equation package associated with a most favorableinterpretation. For example, the processing module applies rules (i.e.,inference, pragmatic engine, utilizing the identifiers of the identigensto match against known valid combinations of identifiers of entigens) toreduce the number of permutations of the sequential combinations ofidentigens to identify at least one equation package. Non-survivingequation packages are eliminated the compare unfavorably to pairingrules.

The method continues at step 659 where the processing module generates aquery response to the surviving equation package, where the survivingequation package is transformed to produce query knowledge for locatingthe portion of a knowledge database that includes an answer to thequery. As an example of generating the query response, the processingmodule interprets the surviving the equation package in accordance withanswer rules to produce the query knowledge (e.g., a graphicalrepresentation of knowledge when the knowledge database utilizes agraphical database format).

The processing module accesses fact base information from the knowledgedatabase to identify the portion of the knowledge database associatedwith a favorable comparison of the query knowledge (e.g., favorablecomparison of attributes of the query knowledge to the portion of theknowledge database, aligning favorably comparing entigens withoutconflicting entigens). The processing module extracts an answer from theportion of the knowledge database to produce the query response.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIG. 9A is a schematic block diagram of another embodiment of acomputing system that includes planning and outcome content sources 660,the AI server 20-1 of FIG. 1, and the user device 12-1 of FIG. 1. Theplanning and outcome content sources 660 includes the content sources16-1 through 16-N of FIG. 1. The AI server 20-1 includes the processingmodule 50-1 of FIG. 2 and the SS memory 96 of FIG. 2. The processingmodule 50-1 includes the collections module 120 of FIG. 4A, the IEImodule 122 of FIG. 4A, and the query module 124 of FIG. 4A. Generally,an embodiment of this invention presents solutions where the computingsystem 10 supports identifying best practices.

The identifying of the best practices includes a series of steps. Forexample, a first step includes facilitating gathering planning andoutcome content based on one or more planning best practices requests.Planning content may include one or more of estimated outcomes, reports,future forecasts, action plans, etc. and outcome content may include oneor more of closing reports, actual results, action plans successes,action plan failures, etc. The planning best practices requests mayinclude one or more of a plan identifier (ID), outcome ID, planning andoutcome content sources ID, supplemental outcome information, minimumthreshold of quality requirement, type of management function, etc.

As a specific example of the first step to facilitate the gathering ofthe planning and outcome content, the IEI module 122 receives an IEIrequest 244 from the query module 124. The query module 124 generatesthe IEI request 244 based on receiving a planning best practices request662 from the user device 12-1. The planning best practices request 662includes the one or more planning best practice requests.

The IEI module 122 determines whether to gather incremental planning andoutcome content based on a maturity level of associated knowledge of aknowledge base (e.g., fact base information 600 from SS memory 96) andindicates to gather more when the maturity level is low, i.e., littleknowledge associated with the one or more requests. When gathering more,the IEI module 122 identifies one or more content sources of theplanning and outcome content sources 660 based on the one or moreplanning best practice requests (e.g., sources associated with planningand/or outcome content associated with the request).

The IEI module 122 issues a collections request 132 to the collectionsmodule 120, receives a collections response 134 from the collectionsmodule 120, where the collections response 134 includes the incrementalplanning and outcome content. The collections module 120 issues one ormore planning and outcome content requests 664 to the identified contentsources of the planning and outcome content sources 660, receivesplanning and outcome content responses 666 from the planning and outcomecontent sources 666, and generates the collections response 134 based onthe received planning and outcome content responses 666.

A second step of the identifying of the best practices includes IEIprocessing the gathered planning and outcome content to produceincremental knowledge associated with the one or more planning bestpractices requests. As a specific example of the second step, the IEImodule 122 IEI processes the incremental planning and outcome content toproduce the incremental knowledge and facilitates storage of theincremental knowledge as fact base information 600 in the SS memory 96.

A third step of the identifying of the best practices includes locatingknowledge to respond to a particular planning best practices request ofthe one or more planning best practices requests. As a specific exampleof the third step, the IEI module 122 IEI processes the particularplanning best practices request to generate request knowledge. The IEImodule 122 compares the request knowledge to knowledge of the knowledgebase to locate a portion of the knowledge base associated with theparticular planning best practices request (i.e., compare a graphicaldatabase representation of the request to portions of the knowledge baseto locate the portion) and indicates the locating of the knowledge.

A fourth step of the identifying of the best practices includesgenerating a planning best practices response with regards to theparticular planning best practices request. As a specific example of thefourth step, the IEI module 122 utilizes the located portion of theknowledge base to produce the planning best practices response 668. Theplanning best practices 668 includes multiple facets of planning andresults including one or more of planning quality metrics for plans,desired planning practices associated with favorable outcomes (e.g.,practices correlated to desired outcomes), and undesired planningpractices associated with unfavorable outcomes (e.g., biases, lapses,gaps, mismatches, incorrect assumptions, etc.).

Having produced the planning best practices response 668, the IEI module122 generates an IEI response 246 that includes the planning bestpractices response 668. The IEI module 122 sends the IEI response 246 tothe query module 124. The query module 124 sends the planning bestpractices response 668 to the user device 12-1.

FIG. 9B is a logic diagram of an embodiment of a method for identifyingbest practices within a computing system. In particular, a method ispresented for use in conjunction with one or more functions and featuresdescribed in conjunction with FIGS. 1-8D, and also FIG. 9A. The methodincludes step 680 where a processing module of one or more processingmodules of one or more computing devices of the computing systemfacilitates gathering planning and outcome content based on one or moreplanning best practices requests.

The facilitating includes receiving the one or more planning bestpractices request and determining whether to gather incremental planningand outcome content based on a maturity level of the associatedknowledge of the knowledge base (e.g., indicate together more when thematurity level is low, i.e., little knowledge associated with the one ormore requests). When gathering more, the processing module identifiesone or more content sources of planning and outcome content sourcesbased on the one or more planning best practice requests (e.g., sourcesassociated with planning and/or outcome content associated with therequest). The processing module causes issuing of one or more planningand outcome content requests to the identified one or more contentsources and receives the incremental planning and outcome content inresponse to the one or more planning and outcome content request.

The method continues at step 682 where the processing module IEIprocesses the gathered planning and outcome content to produceincremental knowledge associated with the one or more planning bestpractices requests. For example, the processing module IEI processes theincremental planning and content outcome to produce incrementalknowledge and facilitates storage of the incremental knowledge in theknowledge base.

The method continues at step 684 where the processing module locatesknowledge to respond to a particular planning best practices request ofthe one or more planning best practices requests. For example, thelocating includes IEI processing the particular planning best practicesrequest to generate request knowledge. The locating further includescomparing the request knowledge to knowledge of the knowledge base tolocate a portion of the knowledge base associated with the particularplanning best practices request. For instance, compare a graphicaldatabase representation of the request to portions of the knowledge baseto locate the portion of the knowledge base.

The method continues at step 686 where the processing module generates aplanning best practices response with regards to the particular planningbest practices request. For example, the generating includes utilizingthe located portion of the knowledge base to produce the planning bestpractices response. The response includes multiple facets of planningand results including one or more of planning quality metrics for plans,desired planning practices associated with favorable outcomes (i.e.,practices correlated to desired outcomes), and undesired planningpractices associated with unfavorable outcomes (i.e., biases, lapses,gaps, mismatches, incorrect assumptions, etc.). In an embodiment, theprocessing module sends the planning best practices response to arequesting entity. Alternatively, or in addition to, the processingmodule ingests the planning best practices response as new content toproduce further incremental knowledge for integration with the knowledgebase to produce an updated knowledge base.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIG. 10A is a schematic block diagram of another embodiment of acomputing system that includes open query content sources 700, the AIserver 20-1 of FIG. 1, and the user device 12-1 of FIG. 1. The openquery content sources 700 includes the content sources 16-1 through 16-Nof FIG. 1. The AI server 20-1 includes the processing module 50-1 ofFIG. 2 and the SS memory 96 of FIG. 2. The processing module 50-1includes the collections module 120 of FIG. 4A, the IEI module 122 ofFIG. 4A, and the query module 124 of FIG. 4A. Generally, an embodimentof this invention presents solutions where the computing system 10supports providing a query dashboard.

The providing of the query dashboard includes a series of steps. Forexample, a first step includes facilitating gathering incrementalcontent associated with one or more open queries to a knowledge base. Asa specific example of the first step, the IEI module 122 receives one ormore IEI requests 244 from the query module 124. The query module 124generates each of the one or more IEI requests 244 based on queriesextracted from one or more query requests 136 from the user device 12-1and identifies one or more content sources 16-1 through 16-N of the openquery content sources 700 associated with desired open query contentassociated with one or more of the queries. For instance, IEI process aquery to produce query knowledge, compare the query knowledge to factbase information 600 from the SS memory 96 that includes storedknowledge, indicate to gather the Incremental content when thecomparison is unfavorable.

The query module 124 issues a collections request 132 to the collectionsmodule 120, where the collections request 132 includes the identity ofthe one or more content sources 16-1 through 16-N and content identifierinformation. The collections module 120 issues one or more open querycontent request 704 to the identified content sources of the open querycontent sources 700 and receives a collections response 134 from thecollections module 120. The collections response 134 includes theincremental content. The collections module 120 extracts the incrementalcontent from one or more open query content responses 706 received fromthe open query content sources 700.

A second step of the providing of the query dashboard includesprocessing the incremental content to update the knowledge base withincremental knowledge. As a specific example of the second step, the IEImodule 122 IEI processes the incremental content to produce theincremental knowledge (i.e. for each word of the incremental content,identify a group of identigens, for each pairwise grouping of sequentialidentigens, identify entigens of each of the group of identigens inaccordance with rules to produce a sequence of entigens). The IEI module122 integrates one or more portions of the sequence of entigens withentigen representations of the knowledge base to produce the updatedknowledge base.

A third step of the providing of the query dashboard includes, for afirst open query, generating query dashboard information. As a specificexample of the third step, the IEI module 122 analyzes a status of theopen query to include one or more of the query, previous interim queryresponses, a final query response, a quality metric associated with aresponse, an estimated time to next response, one or more contentdescriptors associated with content required but not yet received tofacilitate a response, a quality level of the open query (i.e., maturitylevel of an interim response), and a suggested shift in an open query tofacilitate producing a favorable response (i.e., suggested rewording ofa question).

A fourth step of the providing of the query dashboard includesoutputting, to a requesting entity associated with the first open query,one or more of the query dashboard information and a query response tothe first open query. As a specific example of the fourth step, when thematurity level of the current response to the first open query isunfavorable, the IEI module 122 issues an IEI response 246 to the querymodule 124. The IEI response 246 includes the query dashboardinformation.

The query module 124 issues a query dashboard response 708 to the userdevice 12-1 associated with the first open query, otherwise, when thematurity level of the current response to the first open query isfavorable, the IEI module 122 issues the IEI response 246 to the querymodule 124. The IEI response 246 includes a query response (i.e., afinal answer). The query module 124 issues a query response 140 to theuser device 12-1. The query response 140 includes the query response.

FIG. 10B is a logic diagram of an embodiment of a method for providing aquery dashboard within a computing system. In particular, a method ispresented for use in conjunction with one or more functions and featuresdescribed in conjunction with FIGS. 1-8D, and also FIG. 10A. The methodincludes step 730 where a processing module of one or more processingmodules of one or more computing devices of the computing systemfacilitates gathering incremental content associated with one or moreopen queries to a knowledge base.

The facilitating includes one or more of receiving queries andidentifying one or more content sources of open query content sourcesassociated with desired open query content associated with one or moreof the queries (i.e., IEI process a query to produce query knowledge,compare the query knowledge to fact base information of the knowledgebase, indicate together the incremental content when the comparison isunfavorable). The facilitating further includes causing the issuing ofone or more open query content requests to the identified contentsources of the open query content sources, and receiving one or moreopen query content responses that includes the incremental content.

The method continues at step 732 where the processing module processesthe incremental content to update the knowledge base with incrementalknowledge. For example, the processing module IEI processes theincremental content to produce the incremental knowledge (i.e., for eachword of the incremental content, identify a group of identigens, foreach pairwise grouping of sequential identigens, identify entigens ofeach of the group of identigens in accordance with rules to produce asequence of entigens). The processing module integrates one or moreportions of the sequence of entigens with entigen representations of theknowledge base to produce the updated knowledge base.

The method continues at step 734 where, for a first open query, theprocessing module generates query dashboard information. For example,the processing module analyzes a status of the first open query toinclude one or more of the query, previous interim query responses, afinal query response, and a quality metric associated with a response.The first open query may further include an estimated time to nextresponses, one or more content descriptors associated with contentrequired but not yet received to facilitate a response, a quality levelof the open query (i.e., a maturity level of an interim response), and asuggested shift in an open query to facilitate producing the favorableresponse (i.e., suggested rewording of a question).

The method continues at step 736 where the processing module outputs, toa requesting entity associated with the first open query, one or more ofthe query dashboard information and the query response to the first openquery. For example, when the maturity level of the current response tothe first open query is unfavorable, the processing module issues aquery dashboard response to the requesting entity associated with thefirst open query, otherwise, when the maturity level of the currentresponse to the first open query is favorable, the processing moduleissues a query response (i.e., a final answer) to the requesting entity.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIG. 11A is a schematic block diagram of another embodiment of acomputing system that includes new content sources 750, the AI server20-1 of FIG. 1, and the user device 12-1 of FIG. 1. The new contentsources 750 includes the content sources 16-1 through 16-N of FIG. 1.The AI server 20-1 includes the processing module 50-1 of FIG. 2 and theSS memory 96 of FIG. 2. The processing module 50-1 includes thecollections module 120 of FIG. 4A, the IEI module 122 of FIG. 4A, andthe query module 124 of FIG. 4A. Generally, an embodiment of thisinvention presents solutions where the computing system 10 supportsprocessing a suspended query.

The processing of the suspended query includes a series of steps. Forexample, a first step includes determining to suspend an open query whena maturity level of a portion of a knowledge base associated with theopen query is unfavorable. As a specific example of the first step, theIEI module 122 receives an IEI request 244 from the query module 124.The query module 124 receives an initial query request 752 from the userdevice 12-1 and facilitates acquisition of knowledge that would providea basis of a query response to the open query of the initial queryresponse 752 (e.g., identify stored knowledge of the knowledge base fromfact base information 600 of the fact base 592 of the SS memory 96 andmay gather content associated with the open query for generation of theincremental knowledge to update the knowledge base).

When a knowledge acquisition time frame expires, the IEI module 122indicates to suspend the open query when the maturity level of theacquired knowledge of the knowledge base that would provide the basis ofthe query response to the open query is less than a low maturitythreshold level (i.e., not enough knowledge has been acquired at the endof the timeframe). The IEI module 122 generates query metadata 751 forstorage in the SS memory 96. The query metadata 751 includes one or moreof an ID of the suspended query, timeframe of query suspension, thequery, ID of previous content source(s), desired incremental content,desired response quality level, content trigger, query responserecipient(s), etc.

A second step of the processing of the suspended query includesdetermining to reactivate the suspended query when detecting an enablingcondition to support generating a favorable query response. The enablingcondition includes at least one of detecting that the maturity level ofthe portion of the knowledge base associated with the open query isfavorable and detecting that new content is now available to facilitateupdating of the knowledge base such that the maturity level of theportion of the knowledge base is favorable. As a specific example of thesecond step, the IEI module 122, when updating the knowledge base,favorably compares newly acquired content to a content trigger of thequery metadata 751, retrieved from the SS memory 96, where the contenttrigger is associated with the suspended query, and indicates toreactivate upon the favorable comparison when improving the maturitylevel of the portion of the knowledge base associated with the query.

Alternatively, or in addition to, IEI module 122 receives a collectionsresponse 134 from the collections module 120 that includes new contentthat compares favorably to the content trigger. The collections module120 receives new content responses 756 from the new content sources 750in response to new content requests 754 issued to the new contentsources 750 by the collections module 120 based on receiving acollections request 132 from the IEI module 122. The IEI module 122issues the collections request 132 based on extraction of a descriptorof the desired incremental content from the query metadata 751 of thesuspended query. The IEI module 122 IEI processes the incrementalcontent to update the knowledge base (i.e., update the fact base 592),and where the IEI module 122 indicates to reactivate when the updatedknowledge base may be utilized to produce the favorable query response.

A third step of the processing of the suspended query includes, whenreactivating the suspended query, facilitate generating the favorablequery response based on an updated knowledge base. As a specific exampleof the third step, the IEI module 122 processes the query to producequery knowledge, compares the query knowledge to the updated knowledgebase, and extracts query response information to produce the favorablequery response.

A fourth step of the processing of the suspended query includesoutputting the favorable query response to at least one query responserecipient. As a specific example of the fourth step, the IEI module 122identifies, based on the query metadata 751, an identifier of at leastone query response recipient, and issues an IEI response 246 to thequery module 124. The IEI response 246 includes the favorable queryresponse and the identifier of the at least one query response recipient(i.e., the user device 12-1). The query module 124 issues a delayedquery response 758 to the user device 12-1. The delayed query response758 includes the favorable query response.

FIG. 11B is a logic diagram of an embodiment of a method for processinga suspended query within a computing system. In particular, a method ispresented for use in conjunction with one or more functions and featuresdescribed in conjunction with FIGS. 1-8D, and also FIG. 11A. The methodincludes step 760 where a processing module of one or more processingmodules of one or more computing devices of the computing systemdetermines to suspend an open query based on the maturity level of aportion of a knowledge base associated with the open query. For example,the processing module receives an initial query request and facilitatesacquisition of knowledge that would provide a basis of a query responseto the open query of the initial query response (e.g., identify storedknowledge of the knowledge base and may gather content associated withthe open query for generation of the incremental knowledge to update theknowledge base).

When a knowledge acquisition time frame expires, the processing moduleindicates to suspend the open query when the maturity level of theacquired knowledge of the knowledge base that would provide the basis ofthe query response to the open query is less than a low maturitythreshold level (i.e., not enough knowledge has been acquired at the endof the timeframe). The processing module generates query metadata forstorage. The query metadata includes one or more of an identifier (ID)of the suspended query, timeframe of query suspension, the query, ID ofprevious content source(s), desired incremental content, desiredresponse quality level, content trigger, query response recipient(s),etc.

The method continues at step 762 where the processing module determinesto reactivate the suspended query when detecting an enabling conditionto support generating the favorable query response. For example, whenupdating the knowledge base, the processing module favorably comparesnewly acquired content to a content trigger of the query metadata. Thecontent trigger is associated with the suspended query. The processingmodule indicates to reactivate upon the favorable comparison whenimproving the maturity level of the portion of the knowledge baseassociated with the query.

Alternatively, or in addition to, the processing module receives newcontent that compares favorably to the content trigger, where newcontent responses are received from new content sources in response tonew content requests issued to the new content sources, where the newcontent requests are based on extraction of a descriptor of the desiredincremental content from the query metadata of the suspended query. Theprocessing module IEI processes the incremental content to update theknowledge, where the processing module indicates to reactivate thesuspended query when the updated knowledge base may be utilized toproduce the favorable query response.

When reactivating the suspended query, the method continues at step 764where the processing module facilitates generating the favorable queryresponse based on the updated knowledge base. The facilitating includesprocessing the query to produce query knowledge, comparing the queryknowledge to the updated knowledge base, and extracting query responseinformation to produce the favorable query response.

The method continues at step 766 where the processing module outputs thefavorable query response to at least one query response recipient. Forexample, the processing module identifies, based on the query metadata,an identifier of at least one query response recipient, issues thefavorable query response, based on the identifier of the at least onequery response recipient, to the at least one query response recipient.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIGS. 12A-D are schematic block diagrams of another embodiment of acomputing system illustrating an embodiment of a method for collectingcontent to remedy potentially incomplete and/or incorrect knowledge. Thecomputing system includes the content ingestion module 300 of FIG. 5E,the element identification module 302 of FIG. 5E, the interpretationmodule 304 of FIG. 5E, the answer resolution module 306 of FIG. 5E, acorrective content source 780, and a knowledge database 782. In anembodiment, the corrective content source 780 is implemented utilizingone or more of the content sources 16-1 through 16-N of FIG. 1. In anembodiment, the knowledge database 782 is implemented utilizing the factbase 592 of FIG. 8A.

FIG. 12A illustrates an example of the method of the collecting contentto remedy the potentially incomplete and/or incorrect knowledge wherethe interpretation module 304 detects a defective entigen group 784. Inan embodiment, the defective entigen group 784 is generated based on newcontent received from the element identification module 302. In anotherembodiment, the defective entigen group 784 is recovered from theknowledge database 782.

The knowledge database 782 includes the defective entigen group 784 anda multitude of other entigen groups associated with a variety of topics.The defective entigen group 784 includes a plurality of entigens and oneor more entigen relationships between at least some of the plurality ofentigens. The defective entigen group 784 represents knowledge of atopic of the variety of topics. Examples of the entigen relationshipsincludes “describes”, “acts on”, “is a”, “belongs to”, “did”, and “didto.”

The detecting of the defective entigen group 784 includes a variety ofapproaches. A first approach includes determining that a number ofentigens of the defective entigen group 784 compares unfavorably (e.g.,too few, too many, difference greater than a difference threshold) to anumber of other entigens of another entigen group associated with otherknowledge of another topic. The knowledge database 782 further includesthe other entigen group. For example, the interpretation module 304receives entigen information 786 from the knowledge database 782 thatincludes an entigen group associated with bats and another entigen groupassociated with birds. The interpretation module 304 determines that thebat entigen group has far fewer (e.g., more than the differencethreshold number) entigens than the bird entigen group.

A second approach to detect the defective entigen group 784 includesidentifying an incorrect entigen of the defective entigen group 784. Forexample, the interpretation module 304 detects an entigen that does notbelong to the defective entigen group 784 (e.g., logically inconsistentwith other entigens of the defective entigen group 784).

A third approach to detect the defective entigen group 784 includesidentifying an incorrect entigen relationship between first and secondentigens of the defective entigen group 784. For example, theinterpretation module 304 detects an error in a listed entigenrelationship between entigens of the defective entigen group 784 (e.g.,logically inconsistent with other entigen relationships of the defectiveentigen group 784).

FIG. 12B further illustrates the example of the method of the collectingcontent to remedy the potentially incomplete and/or incorrect knowledgewhere the interpretation module 304 obtains corrective content 790 forthe topic based on the defective entigen group 784. The obtainingincludes a series of steps. A first step includes identifying a defectof the defective entigen group. The defect includes one or more of toofew entigens, too many entigens, and incorrect entigen, and an incorrectentigen relationship. For example, the interpretation module 304identifies the defect as too few entigens associated with the “bat”entigen when the other entigen group associated with birds has many moreentigens.

Having identified the defect, a second step includes the interpretationmodule 304 identifying a content aspect based on the defect. The contentaspect includes at least one of related entigens, entigen types (e.g.,object, characteristic, action), and related entigen relationships. Forexample, the interpretation module 304 identifies the content aspect ascontent associated with one or more of bats, flying bats, bats that aremammals, and what bats eat.

Having identified the content aspect, a third step includes theinterpretation module 304 selecting a content source based on thecontent aspect. The selecting includes one or more of accessing a listof content sources associated with various content aspects, identifyinga content source associated with the defective entigen group 784, andidentifying content sources associated with the content aspect. Forexample, the interpretation module 304 selects the corrective contentsource 780 when the corrective content source 780 is known to includecontent associated with bats.

Having selected the content source, a fourth step includes theinterpretation module 304 obtaining the corrective content from thecontent source based on the content aspect. For example, theinterpretation module 304 issues a content request 788 to the correctivecontent source 780, where the content request 788 specifies contentassociated with bats. In response to the content request 788, thecorrective content source 780 sends the corrective content 790 to thecontent ingestion module 300 for further processing.

FIG. 12C further illustrates the example of the method of the collectingcontent to remedy the potentially incomplete and/or incorrect knowledgewhere the interpretation module generates a corrective entigen group 800based on the corrective content 790. The generating of the correctiveentigen group 800 includes a series of steps. A first step includes thecontent ingestion module 300 receiving and parsing the correctivecontent 790 to produce phrase words 792 that includes a plurality ofwords. For example, when the corrective content 790 includes “black bateats fruit”, the content ingestion module 300 produces the phrase words792 to include “black”, “bat”, “eats”, and “fruit.”

Having received the phrase words 792, a second step of the generatingthe corrective entigen group 800 includes the element identificationmodule 302 identifying a set of identigens for each word of thecorrective content 790 to produce a plurality of sets of identigens(e.g., hereafter interchangeably referred to as sets of identigens 796).A set of identigens of the plurality of sets of identigens representsone or more different meanings of a word of the corrective content 790.

As an example of the identifying the sets of identigens 796, the elementidentification module 302 accesses the knowledge database 782 utilizingthe phrase words 792 to recover identigen information 794. The identigeninformation 794 includes, for each word, a set of associated identigens.A set of identigens of the sets of identigens 796 includes one or moredifferent meanings of a word of the corrective content 790. Forinstance, identigens of a first word of the corrective content 790includes one or more different meanings of the first word. As aparticular instance, meanings of the word “black” includes an identigenno. 1 for “dark-skin people”, an identigen no. 2 for “black color”, andanother identigen no. 3 for “to make black.”

Having received the sets of identigens 796, a third step of thegenerating the corrective entigen group 800 includes the interpretationmodule 304 identifying one valid identigen of each set of identigens ofthe plurality of sets of identigens by applying identigen rules 798 tothe plurality of sets of identigens to produce the corrective entigengroup 800. The corrective entigen group 800 represents a most likelymeaning of the corrective content 790.

As an example of the identifying the one valid identigens of each set ofidentigens, the interpretation module accesses the knowledge database782 to recover the identigen rules 798. The identigens rule 798includes, for each adjacent pair of identigens of each of the sets ofidentigens 796, a rule to indicate validity (e.g., valid, invalid).Having recovered the identigen rule 798, the interpretation module 304applies the identigen rule 798 to the sets of identigens 796 to producethe corrective entigen group 800. In an instance of producing thecorrective entigen group 800, the interpretation module 304 determinesthat the identigen rule 798 indicates that a 2-5 identigen pairing isvalid, a 5-8 identigen pairing is valid, and an 8-9 identigen pairing isvalid to produce the corrective entigen group 800. The correctiveentigen group 800 includes entigens 2, 5, 8, and 9 representing the mostlikely meaning of the corrective content 790 “black bat eats fruit.”

FIG. 12D further illustrates the example of the method of the collectingcontent to remedy the potentially incomplete and/or incorrect knowledgewhere the answer resolution module 306 updates the defective entigengroup 784 utilizing the corrective entigen group 800 to produce acurated entigen group 802. The updating includes a variety ofapproaches.

A first approach of the variety of approaches includes the answerresolution module 306 replacing an incorrect entigen of the defectiveentigen group 784 with a correct entigen of the corrective entigen group800. For example, the answer resolution module 306 replaces the “flies”entigen (e.g., incorrect entigen) with a “crawls” entigen (e.g.,corrective entigen) when bats are known to crawl instead of fly.

A second approach of the variety of approaches includes the answerresolution module 306 updating an incorrect entigen relationship betweenfirst and second entigens of the defective entigen group 784 with acorrect entigen relationship between the first and second entigens ofthe corrective entigen group 800. For example, the answer resolutionmodule 306 replaces a relationship between the “eats” and “insects”entigens of the defective entigen group 784 when that relationshipindicates “is a” with a relationship that indicates “does to” from thecorrective entigen group 800.

A third approach of the variety of approaches includes the answerresolution module 306 augmenting the defective entigen group 784utilizing the corrective entigen group 800 to produce the curatedentigen group 802. For example, the answer resolution module 306attaches the “black” entigen of the corrective entigen group 800 to the“bat” entigen of the defective entigen group 784 and attaches the“fruit” entigen of the corrective entigen group 800 to the “eats”entigen of the defective entigen group 784 to produce the curatedentigen group 802. The overall method described above may repeat until afavorable entigen group associated with bats is produced.

Having produced the curated entigen group 802, the method furtherincludes the answer resolution module 306 storing the curated entigengroup 802 in the knowledge database 782. The storing includes one ofreplacing the defective entigen group 784 of the knowledge database 782with the curated entigen group 802 or augmenting the defective entigengroup 784 of the knowledge database 782 with the curated entigen group802.

The method described above in conjunction with any module canalternatively be performed by any modules of the computing system 10 ofFIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIG. 13A is a schematic block diagram of another embodiment of acomputing system that includes unknown trust content sources 820 and theAI server 20-1 of FIG. 1. The unknown trust content sources 820 includesthe content sources 16-1 through 16-N of FIG. 1. The AI server 20-1includes the processing module 50-1 of FIG. 2 and the SS memory 96 ofFIG. 2. The processing module 50-1 includes the collections module 120of FIG. 4A and the IEI module 122 of FIG. 4A. the SS memory 96 includescurated knowledge 822 and non-curated knowledge 824 Generally, anembodiment of this invention presents solutions where the computingsystem 10 supports curating of new knowledge.

The curating of the new knowledge includes a series of steps. Forexample, a first step includes IEI processing new content to produceincremental knowledge. As a specific example of the first step, the IEImodule 122 issues a collections request 132 to the collections module120, where the collections module 120 issues one or more new contentrequest 826 to content sources 16-1 through 16-N of the unknown trustcontent sources 820. The collections module 120 receives one or more newcontent responses 828 from the unknown trust content sources 820, wherethe new content responses 828 includes the new content (e.g., contentthat has not been curated such that a correctness level is unknown,etc.).

The collections module 120 issues a collections response 134 to the IEImodule 122, where the collections response 134 includes the new content.The IEI module 122 IEI processes the new content to produce theincremental knowledge.

A second step of the curating of the new knowledge includes, when theincremental knowledge is non-redundant with regards to a knowledge base,determining whether the incremental knowledge is true (e.g.,verifiable), false (e.g., verified as false), or unknown (e.g.,unverifiable). As a specific example of the second step, the IEI module122 indicates that the incremental knowledge is non-redundant when acomparison of the incremental knowledge with knowledge of the knowledgebase (e.g., fact base information 600 from the SS memory 96) isunfavorable (e.g., not the same). The IEI module 122 applies one or moreverification tests to the incremental knowledge, indicates whether true,false, or unknown, where the verification tests may further includecomparing to another knowledge base, inferring based on currentknowledge of the knowledge base, and processing a response from atrusted content and/or knowledge source.

A third step of the curating of the new knowledge includes, when theincremental knowledge is true, integrating the incremental knowledgewith the knowledge base as curated knowledge. As a specific example ofthe third step, the IEI module 122 modifies a portion of curatedknowledge 822 to include the incremental knowledge when the incrementalknowledge is true (e.g., recover curated knowledge 822 from the SSmemory 96, integrate the incremental knowledge with the curatedknowledge 822 to produce updated curated knowledge 822 for storage asfact base information 600 in the SS memory 96).

A fourth step of the curating of the new knowledge includes, when theincremental knowledge is unknown, determining whether the incrementalknowledge conflicts with the knowledge base. As a specific example ofthe fourth step, the IEI module 122 compares a portion of the knowledgebase to the incremental knowledge and indicates conflict when thecomparison is unfavorable (e.g., conflicting facts etc.)). For instance,the IEI module 122 compares fact base information 600 recovered from theSS memory 96 to the incremental knowledge and indicates the conflictwhen the comparison indicates a contradiction.

A fifth step of the curating of the new knowledge includes, when theincremental knowledge does not conflict with the knowledge base,integrating the incremental knowledge with the knowledge base as curatedknowledge. As a specific example of the first step, the IEI module 122modifies a portion of curated knowledge 822 to include the incrementalknowledge.

A sixth step of the curating of the new knowledge includes, when theincremental knowledge conflicts with the knowledge base, integrating theincremental knowledge with the knowledge base as non-curated knowledge.As a specific example of the sixth step, the IEI module 122 modifies aportion of non-curated knowledge 824 to include the incrementalknowledge. For instance, the IEI module 122 recovers non-curatedknowledge 824 from the SS memory 96, integrates the incrementalknowledge with the non-curated knowledge 824 to produce updatednon-curated knowledge 824 for storage as fact base information 600 inthe SS memory 96.

FIG. 13B is a logic diagram of an embodiment of a method for curatingnew knowledge within a computing system. In particular, a method ispresented for use in conjunction with one or more functions and featuresdescribed in conjunction with FIGS. 1-8D, and also FIG. 13A. The methodincludes step 850 where a processing module of one or more processingmodules of one or more computing devices of the computing system IEIprocesses new content to produce incremental knowledge, where theincremental knowledge is represented as relationships between entigens.For example, the processing module obtains the new content from one ormore unknown trust content sources and IEI processes the new content toproduce incremental knowledge (e.g., of unknown trust).

When the incremental knowledge is non-redundant with regards to aknowledge base, the method continues at step 852 where the processingmodule determines whether the incremental knowledge is true, false, orunknown. For example, the processing module indicates that theincremental knowledge is non-redundant when a comparison of theincremental knowledge with knowledge of the knowledge base isunfavorable (e.g., not the same), applies one or more verification teststo the incremental knowledge and indicates whether true, false, orunknown. The verification tests include one or more of comparing toanother knowledge base, inferring based on current knowledge of theknowledge base, and processing the response from a trusted contentand/or knowledge source.

When the incremental knowledge is true, the method continues at step 854where the processing module integrates the incremental knowledge withthe knowledge base as curated knowledge. For example, the processingmodule modifies a portion of curated knowledge of the knowledge base toinclude the incremental knowledge.

When the incremental knowledge is unknown, the method continues at step856 where the processing module determines whether the incrementalknowledge conflicts with the knowledge base. For example, the processingmodule compares a portion of the knowledge base to the incrementalknowledge and indicates the conflict when the comparison is unfavorable(e.g., conflicting facts, etc.).

When the incremental knowledge does not conflict with the knowledgebase, the method continues at step 858 where the processing moduleintegrates the incremental knowledge with the knowledge base as curatedknowledge. For example, the processing module modifies a portion ofcurated knowledge of the knowledge base to include the incrementalknowledge.

When the incremental knowledge conflicts with the knowledge base, themethod continues at step 860 where the processing module integrates theincremental knowledge with the knowledge base as non-curated knowledge.For example, the processing module modifies a portion of non-curatedknowledge of the knowledge base to include the incremental knowledge.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIGS. 14A and 14B are schematic block diagrams of another embodiment ofa computing system that includes the content ingestion module 300 ofFIG. 5E, the element identification module 302 of FIG. 5E, theinterpretation module 304 of FIG. 5E, the IEI control module 308 of FIG.5E, and the SS memory 96 of FIG. 2. Generally, an embodiment of thisinvention presents solutions where the computing system 10 supportsprocessing content to produce knowledge utilizing a confidence level.

The processing of the content to produce the knowledge utilizing theconfidence level includes a series of steps. For example, a first stepincludes identifying words of an ingested phrase to produce tokenizedwords. As depicted in FIG. 14A, a specific example of the first stepincludes the content ingestion module 300 comparing words of sourcecontent 310 to dictionary entries to produce formatted content 314 thatincludes identifiers of known words. For instance, the content ingestionmodule 300 identifies words “the”, “black”, “bat”, “eats”, and “fruit”when the ingested phrase includes “The black bat eats fruit.”

A second step of the processing of the content to produce the knowledgeutilizing the confidence level includes, for each tokenized word,identifying one or more identigens that correspond the tokenized word,where each identigen describes one of an object, a characteristic, andan action. As depicted in FIG. 14A, a specific example of the secondstep includes the element identification module 302 performing a look upof identigen identifiers, utilizing an element list 332 and inaccordance with element rules 318, of the one or more identigensassociated with each tokenized word of the formatted content 314 toproduce identified element information 340.

A unique identifier is associated with each of the potential object, thecharacteristic, and action (OCA) associated with a particular tokenizedword. For instance, the element identification module 302 identifies afunctional symbol for “the”, identifies a single identigen for “black”,identifies two identigens for “bat” (e.g., baseball bat and flying bat),identifies a single identigen for “eats”, and identifies a singleidentigen for “fruit.”

A third step of the processing of the content to produce the knowledgeutilizing the confidence level includes, for each permutation ofsequential combinations of identigens, generating a correspondingequation package (i.e., candidate interpretation), where the equationpackage includes a sequential linking of pairs of identigens. Eachsequential linking pairs a preceding identigen to a next identigen. Anequation element describes a relationship between paired identigens(OCAs) such as describes, acts on, is a, belongs to, did, did to, etc.

Multiple OCAs occur for a common word when the word has multiplemeanings (e.g., a baseball bat, a flying bat). As depicted in FIG. 14A,a specific example of the third step includes the interpretation module304, for each permutation of identigens of each tokenized word of theidentified element information 340, including with all otherpermutations of all other tokenized words to generate the equationpackages in accordance with interpretation rules 320 and a groupingslist 334. For instance, the interpretation module 304 produces a firstequation package that includes a first pairing of a black bat (e.g.,flying bat), the second pairing of bat eats (e.g., the flying bat eats),and a third pairing of eats fruit, and the interpretation module 304produces a second equation package that includes a first pairing of ablack bat (e.g., baseball bat), the second pairing of bat eats (e.g.,the baseball bat eats), and a third pairing of eats fruit.

A fourth step of the processing of the content to produce the knowledgeutilizing the confidence level includes selecting a surviving equationpackage associated with a most favorable confidence level. As depictedin FIG. 14A, a specific example of the fourth step includes theinterpretation module 304 applying interpretation rules 320 (i.e.,inference, pragmatic engine, utilizing the identifiers of the identigensto match against known valid combinations of identifiers of entigens) toreduce the number of permutations of the sequential combinations ofidentigens to produce interpreted information 344 that includesidentification of at least one equation package as a survivinginterpretation 880 (e.g., higher confidence level).

Non-surviving equation packages are eliminated that compare unfavorablyto pairing rules and/or are associated with an unfavorable confidencelevel to produce a non-surviving interpretation 882 (e.g., lowerconfidence level), where a confidence level is assigned to each equationpackage such that a higher confidence level indicates that the equationpackage includes equation elements that are substantially the same as inother equation packages (i.e., more consistency). For instance, theinterpretation module 304 eliminates the equation package that includesthe second pairing indicating that the “baseball bat eats” which isinconsistent with one or more of the groupings list 334 and theinterpretation rules 320 and selects the equation package associatedwith the “flying bat eats” which is favorably consistent with the one ormore of the groupings list 334 and the interpretation rules 320.

A fifth step of the processing of the content to produce the knowledgeutilizing the confidence level includes integrating knowledge of thesurviving equation package into a knowledge base. For example,integrating at least a portion of the reduced OCA combinations into agraphical database to produce updated knowledge. As another example, theportion of the reduced OCA combinations may be translated into rows andcolumns entries when utilizing a rows and columns database rather than agraphical database.

As depicted in FIG. 14B, a specific example of the fifth step includesthe IEI control module 308 recovering fact base information 600 from SSmemory 96 to identify a portion of the knowledge base for potentialmodification utilizing the OCAs of the surviving equation package (i.e.,compare a pattern of relationships between the OCAs of the survivingequation package from the interpreted information 344 to relationshipsof OCAs of the portion of the knowledge base including new confidencelevels). The steps further includes the IEI control module 308determining modifications (e.g., additions, subtractions, furtherclarifications required when information is complex, etc.) to theportion of the knowledge base based on the new confidence levels (i.e.,add the element “black” as a “describes” relationship of an existing batOCA and add the element “fruit” as a eats “does to” relationship), andimplement the modifications to the portion of the fact base information600 to produce updated fact base information 608 for storage in the SSmemory 96.

FIG. 14C is a logic diagram of an embodiment of a method for processingcontent to produce knowledge utilizing a confidence level within acomputing system. In particular, a method is presented for use inconjunction with one or more functions and features described inconjunction with FIGS. 1-8D, 14A, and also FIG. 14B. The method includesstep 900 where a processing module of one or more processing modules ofone or more computing devices of the computing system identifies wordsof an ingested phrase to produce tokenized words. The identifiedincludes comparing words to known words of dictionary entries to produceidentifiers of known words.

For each tokenized word, the method continues at step 902 where theprocessing module identifies one or more identigens that corresponds tothe tokenized word, where each identigen describes one of an object, acharacteristic, and an action. The identifying includes performing alookup of identifiers of the one or more identigens associated with eachtokenized word, where the different identifiers associated with each ofthe potential object, the characteristic, and the action associated withthe tokenized word.

For each permutation of sequential combinations of identigens, themethod continues at step 904 where the processing module generates acorresponding equation package, where the equation package includes asequential linking of pairs of identigens. Each sequential linking pairsa preceding identigen to a next identigen. An equation element describesa relationship and confidence level between paired identigens. Forexample, for each permutation of identigens of each tokenized word, theprocessing module includes with all other permutations of all othertokenized words to generate the equation packages. A confidence level isassigned to each equation package such that a higher confidence levelindicates that the equation package is associated with equation elementsthat are substantially the same as in other equation packages (i.e.,more consistency).

The method continues at step 906 where the processing module selects asurviving equation package associated with a most favorable equationelement confidence level. For example, the processing module appliesrules (i.e., inference, pragmatic engine, utilizing the identifiers ofthe identigens to match against known valid combinations of identifiersof entigens) to reduce the number of permutations of the sequentialcombinations of identigens to identify at least one equation package.Non-surviving equation packages are eliminated that compare unfavorablyto pairing rules and/or are associated with an unfavorable confidencelevel.

The method continues at step 908 where the processing module integratesknowledge of the surviving equation package into a knowledge base. Forexample, the processing module integrates at least a portion of thereduced OCA combinations into a graphical database to produce updatedknowledge. For instance, the processing module recovers fact baseinformation from storage of the knowledge base to identify a portion ofthe knowledge base for potential modification utilizing the OCAs of thesurviving equation package (i.e., compare a pattern of relationshipsbetween the OCAs of the surviving equation package to relationships ofOCAs of the portion of the knowledge base). The processing moduledetermines modifications (e.g., additions, subtractions, furtherclarifications required when information complex, etc.) to the portionof the knowledge base and implements the modifications to the portion ofthe fact base information to produce updated fact base information forstorage in the portion of the knowledge base.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIGS. 15A and 15B are schematic block diagrams of another embodiment ofa computing system that includes the content ingestion module 300 ofFIG. 5E, the element identification module 302 of FIG. 5E, theinterpretation module 304 of FIG. 5E, the answer resolution module 306of FIG. 5E, and the SS memory 96 of FIG. 2. Generally, an embodiment ofthis invention presents solutions where the computing system 10 supportsfor generating a query response to a query.

The generating of the query response to the query includes a series ofsteps. For example, a first step includes identifying words of aningested query to produce tokenized words. As depicted in FIG. 15A, aspecific example of the first step includes the content ingestion module300 comparing words of query info 138 to dictionary entries to produceformatted content 314 that includes identifiers of known words. Forinstance, the content ingestion module 300 produces identifiers for eachword of the query “what black animal flies and eats fruit and insects?”

A second step of the generating of the query response to the queryincludes, for each tokenized word, identifying one or more identigensthat correspond the tokenized word, where each identigen describes oneof an object, a characteristic, and an action. As depicted in FIG. 15A,a specific example of the second step includes the elementidentification module 302 performing a look up of identifiers, utilizingan element list 332 and in accordance with element rules 318, of the oneor more identigens associated with each tokenized word of the formattedcontent 314 to produce identified element information 340. A uniqueidentifier is associated with each of the potential object, thecharacteristic, and action associated with a particular tokenized word.For instance, the element identification module 302 produces a singleidentigen identifier for each of the black color, an animal, flies,eats, fruit, and insects.

A third step of the generating of the query response to the queryincludes, for each permutation of sequential combinations of identigens,generating a corresponding equation package (i.e., candidateinterpretation), where the equation package includes a sequentiallinking of pairs of identigens. Each sequential linking pairs apreceding identigen to a next identigen. An equation an elementdescribes a relationship between paired identigens (OCAs) such asdescribes, acts on, is a, belongs to, did, did to, etc.

As depicted in FIG. 15A, a specific example of the third step includesthe interpretation module 304, for each permutation of identigens ofeach tokenized word of the identified element information 340,generating the equation packages in accordance with interpretation rules320 and a groupings list 334 to produce a series of equation elementsthat include pairings of identigens. For instance, the interpretationmodule 304 generates a first pairing to describe a black animal, asecond pairing to describe an animal that flies, a third pairing todescribe flies and eats, a fourth pairing to describe eats fruit, and afifth pairing to describe eats fruit and insects.

A fourth step of the generating the query response to the query includesselecting a surviving equation package associated with a most favorableinterpretation. As depicted in FIG. 15A, a specific example of thefourth step includes the interpretation module 304 applying theinterpretation rules 320 (i.e., inference, pragmatic engine, utilizingthe identifiers of the identigens to match against known validcombinations of identifiers of entigens) to reduce the number ofpermutations of the sequential combinations of identigens to produceinterpreted information 344.

The interpreted information 344 includes identification of at least oneequation package as a surviving interpretation 920. Non-survivingequation packages, if any, are eliminated that compare unfavorably topairing rules to produce a non-surviving interpretation.

A fifth step of the generating the query response to the query includesutilizing a knowledge base, generating a query response to the survivingequation package of the query, where attributes of the survivingequation package are compared to attributes of one or more portions ofthe knowledge base to identify a corresponding portion of the knowledgebase associated with a favorable comparison. The comparison includes atleast one of directly comparing attributes to find a favorable match andcomparing the attributes of the surviving equation package to majorattributes and/or reader attribute comparisons of one or more groups ofentigens associated with the one or more portions of the knowledge base.

As depicted in FIG. 15B, a specific example of the fifth step includesthe answer resolution module 306 interpreting the survivinginterpretation 920 of the interpreted information 344 in accordance withanswer rules 322 to generate the attributes of the surviving equationpackage, accessing fact base information 600 from the SS memory 96 toidentify the one or more portions of the knowledge base associated withlikely favorable comparisons of the attributes of the surviving equationpackage to attributes of the groups of entigens, and selecting one ofthe entigen groups based on comparing the attributes of the entigengroup with the attributes of the surviving equation package (i.e., alignfavorable comparison entigens without conflicting entigens).

The fifth step further includes comparing differences of entigen groupmetadata 930 to quickly identify the entigen group with a most favorablecomparison (i.e., while a black animal that eats fruits and insects, apanther does not fly, but a bat flies and eats fruit and insects). Thefifth step further includes generating preliminary answers 354 toinclude a query response to the query (i.e., a bat is a black animalthat flies and eats fruit and insects is associated with entigen group2)).

FIG. 15C is a logic diagram of an embodiment of a method for generatinga query response to a query utilizing groupings within a knowledge basewithin a computing system. In particular, a method is presented for usein conjunction with one or more functions and features described inconjunction with FIGS. 1-8D, 15A, and also FIG. 15B. The method includesstep 940 where a processing module of one or more processing modules ofone or more computing devices of the computing system identifies wordsof an ingested query to produce tokenized words. For example, theprocessing module compares words to known words of dictionary entries toproduce identifiers of known words.

For each tokenized word, the method continues at step 942 where theprocessing module identifies one or more identigens that correspond tothe tokenized word, where each identigen describes one of an object, acharacteristic, and an action. For example, the processing moduleperforms a lookup of identifiers of the one or more identigensassociated with each tokenized word, where different identifiersassociated with each permutation of a potential object, characteristic,and action associated with the tokenized word.

For each permutation of sequential combinations of identigens, themethod continues at step 944 where the processing module generates acorresponding equation package, where the equation package includes asequential linking of pairs of identigens, where each sequential linkingpairs a preceding identigen to a next identigen. An equation elementdescribes a relationship between paired identigens. For example, foreach permutation of identigens of each tokenized word, the processingmodule includes, with all other permutations of all other tokenizedwords, to generate the equation packages.

The method continues at step 946 where the processing module selects asurviving equation package associated with a most favorableinterpretation. For example, the processing module applies rules (i.e.,inference, pragmatic engine, utilizing the identifiers of the identigensto match against known valid combinations of identifiers of entigens) toreduce the number of permutations of the sequential combinations ofidentigens to identify at least one equation package. Non-survivingequation packages are eliminated the compare unfavorably to pairingrules.

The method continues at step 948 where the processing module, utilizingentigen group access to a knowledge base, generates a query response tothe surviving equation package (e.g., the query). Attributes of thesurviving equation package are compared to attributes of one or moreportions of the knowledge base to identify a corresponding portion ofthe knowledge base associated with a favorable comparison. Thecomparison includes at least one of directly comparing attributes tofind a favorable match and comparing the attributes of the survivingequation package to major attributes and/or reader attribute comparisonsof one or more groups of entigens associated with the one or moreportions of the knowledge base.

As an example of generating the query response, the processing moduleinterprets the surviving the equation package in accordance with answerrules to generate the attributes of the surviving equation package,accesses fact base information of the knowledge base to identify the oneor more portions of the knowledge base associated with likely favorablecomparisons of the attributes of the surviving equation package toattributes of the groups of entigens, and selects one of the entigengroups based on comparing the attributes of the entigen group with theattributes of the surviving equation package (i.e., align favorablecomparison entigens without conflicting entigens). The example furtherincludes comparing differences of entigen group metadata to quicklyidentify the entigen group with a most favorable comparison, andgenerates preliminary answers to include a query response to the query.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to diverse content sources 960

FIG. 16A is a schematic block diagram of another embodiment of acomputing system that includes diverse content sources 960, the AIserver 20-1 of FIG. 1, and the user device 12-1 of FIG. 1. The diversecontent sources 960 includes the content sources 16-1 through 16-N ofFIG. 1. The AI server 20-1 includes the processing module 50-1 of FIG. 2and the SS memory 96 of FIG. 2. The processing module 50-1 includes thecollections module 120 of FIG. 4A, the IEI module 122 of FIG. 4A, andthe query module 124 of FIG. 4A. Generally, an embodiment of thisinvention presents solutions where the computing system 10 supportsgenerating a query response to a query utilizing a confidence level.

The generating of the query response to the query utilizing theconfidence level includes a series of steps. For example, the first stepincludes determining an approach to generating a query response to aquery, where the query response is generated utilizing knowledgecontained in two or more knowledge bases. Diverse content is ingested toproduce knowledge stored in the two for more knowledge bases.

As a specific example of the first step, based on one or more of apredetermination, historical quality levels of query responses, andextracting guidance from the query, where when extracting the guidancefrom the query, the IEI module 122 receives an IEI request 244 from thequery module 124. The IEI module 122 extracts the approach from a queryof the IEI request 244, where the query module 124 receives a qualifiedquery request 962 from the user device 12-1. The qualified query request962 includes one or more of a desired minimum confidence level andidentifier of a query response selection approach (i.e., a highestconfidence level, a blended response, etc.).

When the diverse content is ingested, further steps includes the IEImodule 122 issuing a collections request 132 to the collections module120, where the collections module 120 issues one or more diverse contentrequests 964 to content sources 16-1 through 16-N of the diverse contentsources 960. The collections module 120 issues a collections response134 to the IEI module 122 based on received diverse content responses966 that include the diverse content.

The IEI module 122 extracts the diverse content from the collectionsresponse 134. The IEI module 122 IEI processes the source content toproduce incremental knowledge for storage as fact base information 600-1and/or 600-2 in one or more of the SS memories 96-1 and 96-2 (i.e.,mostly a first type of knowledge stored in a first memory and mostly asecond type of knowledge stored in a second memory where the first typeof knowledge and a second type of knowledge may overlap).

A second step of the generating of the query response to the queryutilizing the confidence level includes generating, for each knowledgebase of the two or more knowledge bases, a corresponding query responseutilizing the approach to generating the query response. As a specificexample of the second step, for each knowledge base, the IEI module 122IEI processes the query from the qualified query request 962 to producequery knowledge. The IEI module 122 accesses a portion of the knowledgebase corresponding to the query knowledge to produce a correspondingquery response (i.e., the IEI module 122 recovers fact base information600-1 from the SS memory 96-1 for a first knowledge base for locating ofthe first portion of the first knowledge base that corresponds favorablyto the query knowledge, etc.).

A third step of generating the query response to the query utilizing theconfidence level includes generating, for each query response, acorresponding confidence level. As a specific example of the third step,the IEI module 122 compares each query response to each other queryresponse to generate comparisons and indicates a higher confidence levelfor a particular query response when a comparison of the particularquery response to other query responses are more favorable (e.g., moresimilar responses).

A fourth step of the generating the query response to the queryutilizing the confidence level includes generating a qualified queryresponse utilizing the query responses and based on the approach togenerating the query response and based on the corresponding confidencelevels. As a specific example of the fourth step, the IEI module 122generates the qualified query response in accordance with the approach(i.e., selecting a query response with the highest confidence level,combining query responses when confidence levels are favorable andsubtle differences exists in two or more query responses of thecombination, etc.). The IEI module 122 issues an IEI response 246 to thequery module 124, where the IEI response 246 includes the qualifiedquery response 968. The query module 124 sends the qualified queryresponse 968 to the user device 12-1.

FIG. 16B is a logic diagram of an embodiment of a method for generatinga query response to a query utilizing a confidence level within acomputing system. In particular, a method is presented for use inconjunction with one or more functions and features described inconjunction with FIGS. 1-8D, and also FIG. 16A. The method includes step980 where a processing module of one or more processing modules of oneor more computing devices of the computing system determines an approachto generating a query response to a query.

The query response is generated utilizing knowledge contained in two ormore knowledge bases. Diverse content may be ingested to produceknowledge stored in the two or more knowledge bases. For example, theprocessing module, based on one or more of a predetermination,historical quality levels of query responses, and extracting guidancefrom the query, where when extracting the guidance from the query,receives a qualified query request. The processing module extracts theapproach, where the qualified query request includes one or more of thequery, a desired minimum confidence level, an identifier of a queryresponse selection approach (i.e., a highest confidence level, a blendedresponse, etc.).

When the diverse content is ingested, the processing module causesissuing of one or more diverse content requests to content sources ofdiverse content sources and receives diverse content responses thatinclude the diverse content. The processing module extracts the diversecontent from diverse content responses and IEI processes the diversecontent to produce incremental knowledge for storage as fact baseinformation in storage of the two or more knowledge bases (i.e., mostlya first type of knowledge stored in a first portion of the storage andmostly a second type of knowledge stored in a second portion of thestorage where the first type of knowledge and a second type of knowledgemay overlap).

For each knowledge base of the two or more knowledge bases, the methodcontinues at step 982 where the processing module generates, utilizingthe approach, a corresponding query response. For example, for eachknowledge base, the processing module IEI processes the query from thequalified query request to produce query knowledge and accesses aportion of the knowledge base corresponding to the query knowledge toproduce a corresponding query response (i.e., processing module recoversfact base information from a first portion of storage of a firstknowledge base for locating of the first portion of the first knowledgebase that corresponds favorably to the query knowledge, etc.).

For each query response, the method continues at step 984 where theprocessing module determines a corresponding confidence level. Forexample, the processing module compares each query response to eachother query response to generate comparisons and indicates a higherconfidence level for a particular query response when a comparison ofthe particular query response to other query responses are morefavorable (e.g., more similar responses).

The method continues at step 986 for the processing module generates aqualified query response utilizing at least some of the query responsesbased on the approach and the confidence levels. For example, theprocessing module generates the qualified query response in accordancewith the approach (i.e., selecting a query response with a highestconfidence level, combining query responses one confidence levels arefavorable and subtle differences exist in two or more query responses ofthe combinations, etc.), and issues the qualified query response to aquery response recipient.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIGS. 17A and 17B are schematic block diagrams of another embodiment ofa computing system that includes the content ingestion module 300 ofFIG. 5E, the element identification module 302 of FIG. 5E, theinterpretation module 304 of FIG. 5E, the IEI control module 308 of FIG.5E, and the SS memory 96 of FIG. 2. Generally, an embodiment of thisinvention presents solutions where the computing system 10 supportsprocessing content to produce knowledge utilizing a certainty level.

The processing of the content to produce the knowledge utilizing thecertainty level includes a series of steps. For example, a first stepincludes identifying words of an ingested phrase to produce tokenizedwords. As depicted in FIG. 17A, a specific example of the first stepincludes the content ingestion module 300 comparing words of sourcecontent 310 to dictionary entries to produce formatted content 314 thatincludes identifiers of known words. For instance, the content ingestionmodule 300 identifies words “the”, “black”, “bat”, “eats”, and “fruit”when the ingested phrase includes “The black bat eats fruit.”

A second step of the processing of the content to produce the knowledgeutilizing the certainty level includes, for each tokenized word,identifying one or more identigens that correspond the tokenized word,where each identigen describes one of an object, a characteristic, andan action. As depicted in FIG. 17A, a specific example of the secondstep includes the element identification module 302 performing a look upof identigen identifiers, utilizing an element list 332 and inaccordance with element rules 318, of the one or more identigensassociated with each tokenized word of the formatted content 314 toproduce identified element information 340.

A unique identifier is associated with each of the potential object, thecharacteristic, and action (OCA) associated with a particular tokenizedword. For instance, the element identification module 302 identifies afunctional symbol for “the”, identifies a single identigen for “black”,identifies two identigens for “bat” (e.g., baseball bat and flying bat),identifies a single identigen for “eats”, and identifies a singleidentigen for “fruit.”

A third step of the processing of the content to produce the knowledgeutilizing the certainty level includes, for each permutation ofsequential combinations of identigens, generating a correspondingequation package (i.e., candidate interpretation). The equation packageincludes a sequential linking of pairs of identigens (e.g.,relationships, probability level of accuracy of the relationship toprovide the certainty level of the relationship).

Each sequential linking pairs a preceding identigen to a next identigen.An equation element describes a relationship between paired identigens(OCAs) such as describes, acts on, is a, belongs to, did, did to, etc.Multiple OCAs occur for a common word when the word has multiplemeanings (e.g., a baseball bat, a flying bat).

As depicted in FIG. 17A, a specific example of the third step includesthe interpretation module 304, for each permutation of identigens ofeach tokenized word of the identified element information 340, includingwith all other permutations of all other tokenized words to generate theequation packages. The packages include pairings certainty levels, inaccordance with interpretation rules 320, and a groupings list 334. Forinstance, the interpretation module 304 produces a first equationpackage that includes a first pairing of a black bat (e.g., flying batwith a higher pairing certainty level), the second pairing of bat eats(e.g., the flying bat eats, with a higher pairing certainty level), anda third pairing of eats fruit. The interpretation module 304 produces asecond equation package that includes a first pairing of a black bat(e.g., baseball bat, with a neutral pairing certainty level), the secondpairing of bat eats (e.g., the baseball bat eats, with a lower pairingcertainty level), and a third pairing of eats fruit.

A fourth step of the processing of the content to produce the knowledgeutilizing the certainty level includes selecting a surviving equationpackage associated with a most favorable confidence level. As depictedin FIG. 17A, a specific example of the fourth step includes theinterpretation module 304 applying interpretation rules 320 (i.e.,inference, pragmatic engine, utilizing the identifiers of the identigensto match against known valid combinations of identifiers of entigens) toreduce the number of permutations of the sequential combinations ofidentigens to produce interpreted information 344.

The interpreted information 344 includes identification of at least oneequation package as a surviving interpretation 1000 (e.g., higherpairings certainty level), where non-surviving equation packages areeliminated that compare unfavorably to pairing rules and/or areassociated with an unfavorable pairings certainty levels to produce anon-surviving interpretation 1002 (e.g., lower pairings certaintylevel). An overall pairings certainty level is assigned to each equationpackage based on pairing certainty levels of each pairing, such that ahigher pairing certainty level indicates that equation package with ahigher probability of correctness. For instance, the interpretationmodule 304 eliminates the equation package that includes the secondpairing indicating that the “baseball bat eats” which is inconsistentwith a pairings certainty level of one or more of the groupings list 334and the interpretation rules 320 and selects the equation packageassociated with the “flying bat eats” which is favorably consistent withthe one or more of the pairing certainty level of the groupings list 334and the interpretation rules 320.

A fifth step of the processing of the content to produce the knowledgeutilizing the confidence level includes integrating knowledge of thesurviving equation package into a knowledge base. For example,integrating at least a portion of the reduced OCA combinations into agraphical database to produce updated knowledge. As another example, theportion of the reduced OCA combinations may be translated into rows andcolumns entries when utilizing a rows and columns database rather than agraphical database.

As depicted in FIG. 17B, a specific example of the fifth step includesthe IEI control module 308 recovering fact base information 600 from SSmemory 96 to identify a portion of the knowledge base for potentialmodification utilizing the OCAs of the surviving interpretation 1000(i.e., compare a pattern of relationships between the OCAs of thesurviving interpretation 1000 from the interpreted information 344 torelationships of OCAs of the portion of the knowledge base includingpotentially new pairings certainty levels). The fifth step furtherincludes determining modifications (e.g., additions, subtractions,further clarifications required when information is complex, etc.) tothe portion of the knowledge base based on the new pairings certaintylevels. For instance, the IEI control module 308 causes adding theelement “black” as a “describes” relationship of an existing bat OCA andadding the element “fruit” as a eats “does to” relationship to implementthe modifications to the portion of the fact base information 600 toproduce updated fact base information 608 for storage in the SS memory96.

FIG. 17C is a logic diagram of an embodiment of a method for processingcontent to produce knowledge utilizing a certainty level within acomputing system. In particular, a method is presented for use inconjunction with one or more functions and features described inconjunction with FIGS. 1-8D, 17A, and also FIG. 17B. The method includesstep 1010 where a processing module of one or more processing modules ofone or more computing devices of the computing system identifies wordsof an ingested phrase to produce tokenized words. The identifyingincludes comparing words to known words of dictionary entries to produceidentifiers of known words.

For each tokenized word, the method continues at step 1012 where theprocessing module identifies one or more identigens that corresponds tothe tokenized word, where each identigen describes one of an object, acharacteristic, and an action. The identifying includes performing alookup of identifiers of the one or more identigens associated with eachtokenized word, where the different identifiers associated with each ofthe potential object, the characteristic, and the action associated withthe tokenized word.

The method continues at step 1014 where the processing module, for eachpermutation of sequential combinations of identigens, generates acorresponding equation package. The equation package includes asequential linking of pairs of identigens, where each sequential linkingpairs a preceding identigen to a next identigen, and where an equationelement describes a relationship and certainty level between pairedidentigens. For example, for each permutation of identigens of eachtokenized word, the processing module includes, with all otherpermutations of all other tokenized words to generate the equationpackages, where the equation elements include probability of correctnessbased on certainty levels of pairings of identigens.

The method continues at step 1016 where the processing module selects asurviving equation package associated with most favorable equationelement certainty levels. For example, the processing module appliesrules (i.e., inference, pragmatic engine) utilizing the identifiers ofthe identigens to match against known valid combinations of identifiersof entigens, to reduce the number of permutations of the sequentialcombinations of identigens. This identifies at least one equationpackage, where non-surviving equation packages are eliminated thecompare favorably to pairing rules and/or are associated with anunfavorable pairings certainty level to produce a non-survivinginterpretation. An overall certainty level is assigned to each equationpackage based on certainty levels of each pairing, such that a highercertainty level indicates an equation package with a higher probabilityof correctness.

The method continues at step 1018 where the processing module integratesknowledge of the surviving equation package into a knowledge base. Forexample, the processing module integrates at least a portion of thereduced OCA combinations into a graphical database to produce updatedknowledge. The integrating may include recovering fact base informationfrom storage of the knowledge base to identify a portion of theknowledge base for potential modifications utilizing the OCs of thesurviving equation package (i.e., compare a pattern of relationshipsbetween the OCs of the surviving equation package to relationships ofthe OCs of the portion of the knowledge base including potentially newpairing certainty levels).

The integrating further includes determining modifications (e.g.,additions, subtractions, further clarifications required when complexinformation is presented, etc.) to produce the knowledge base that isbased on fit of acceptable pairing certainty levels. The integratingfurther includes implementing the modifications to the portion of thefact base information to produce the updated fact base information forstorage in the portion of the knowledge base.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIGS. 18A and 18B are schematic block diagrams of another embodiment ofa computing system that includes the content ingestion module 300 ofFIG. 5E, the element identification module 302 of FIG. 5E, theinterpretation module 304 of FIG. 5E, the IEI control module 308 of FIG.5E, and the SS memory 96 of FIG. 2. Generally, an embodiment of thisinvention presents solutions where the computing system 10 supportsprocessing content to produce knowledge.

The processing of the content to produce the knowledge includes a seriesof steps. For example, a first step includes identifying words of aningested phrase to produce tokenized words. As depicted in FIG. 18A, aspecific example of the first step includes the content ingestion module300 comparing words of source content 310 to dictionary entries toproduce formatted content 314 that includes identifiers of known words.Alternatively, when a comparison is unfavorable, the temporaryidentifier may be assigned to an unknown word. For instance, the contentingestion module 300 produces identifiers associated with the words“the”, “black”, “bat”, “eats”, and “fruit” when the ingested phraseincludes “The black bat eats fruit”, and generates the formatted content314 to include the identifiers of the words.

A second step of the processing of the content to produce the knowledgeincludes, for each tokenized word, identifying one or more identigensthat correspond the tokenized word. Each identigen describes one of anobject, a characteristic, and an action. As depicted in FIG. 18A, aspecific example of the second step includes the element identificationmodule 302 performing a look up of identigen identifiers, utilizing anelement list 332 and in accordance with element rules 318, of the one ormore identigens associated with each tokenized word of the formattedcontent 314 to produce identified element information 340.

A unique identifier is associated with each of the potential object, thecharacteristic, and the action (OCA) associated with the tokenized word.For instance, the element identification module 302 identifies afunctional symbol for “the”, identifies a single identigen for “black”,identifies two identigens for “bat” (e.g., baseball bat and flying bat),identifies a single identigen for “eats”, and identifies a singleidentigen for “fruit.” When at least one tokenized word is associatedwith multiple identigens, two or more permutations of sequentialcombinations of identigens for each tokenized word result. For example,when “bat” is associated with two identigens, two permutations ofsequential combinations of identigens result for the ingested phrase.

A third step of the processing of the content to produce the knowledgeincludes, for each permutation of sequential combinations of identigens,generating a corresponding equation package (i.e., candidateinterpretation). The equation package includes a sequential linking ofpairs of identigens (e.g., relationships), where each sequential linkingpairs a preceding identigen to a next identigen. An equation elementdescribes a relationship between paired identigens (OCAs) such asdescribes, acts on, is a, belongs to, did, did to, etc. Multiple OCAsoccur for a common word when the word has multiple potential meanings(e.g., a baseball bat, a flying bat).

As depicted in FIG. 18A, a specific example of the third step includesthe interpretation module 304, for each permutation of identigens ofeach tokenized word of the identified element information 340,generating, in accordance with interpretation rules 320 and a groupingslist 334, the equation package. The package includes one or more of theidentifiers of the tokenized words, a list of identifiers of theidentigens of the equation package, a list of pairing identifiers forsequential pairs of identigens, and a quality metric associated witheach sequential pair of identigens (e.g., likelihood of a properinterpretation).

For instance, the interpretation module 304 produces a first equationpackage that includes a first identigen pairing of a black bat (e.g.,flying bat with a higher quality metric level), the second pairing ofbat eats (e.g., the flying bat eats, with a higher quality metriclevel), and a third pairing of eats fruit. The interpretation module 304produces a second equation package that includes a first pairing of ablack bat (e.g., baseball bat, with a neutral quality metric level), thesecond pairing of bat eats (e.g., the baseball bat eats, with a lowerquality metric level), and a third pairing of eats fruit.

A fourth step of the processing of the content to produce the knowledgeincludes selecting a surviving equation package associated with a mostfavorable confidence level. As depicted in FIG. 18A, a specific exampleof the fourth step includes the interpretation module 304 applyinginterpretation rules 320 (i.e., inference, pragmatic engine) utilizingthe identifiers of the identigens to match against known validcombinations of identifiers of entigens to reduce a number ofpermutations of the sequential combinations of identigens to produceinterpreted information 344.

The interpreted information 344 includes identification of at least oneequation package as a surviving interpretation 1030 (e.g., higherquality metric level). Non-surviving equation packages are eliminatedthat compare unfavorably to pairing rules and/or are associated with anunfavorable quality metric levels to produce a non-survivinginterpretation 1032 (e.g., lower quality metric level).

In an embodiment, an overall quality metric level is assigned to eachequation package based on quality metric levels of each pairing, suchthat a higher quality metric level of an equation package indicates ahigher probability of a most favorable interpretation. For instance, theinterpretation module 304 eliminates the equation package that includesthe second pairing indicating that the “baseball bat eats” which isinconsistent with a desired quality metric level of one or more of thegroupings list 334 and the interpretation rules 320 and selects theequation package associated with the “flying bat eats” which isfavorably consistent with the one or more of the quality metric levelsof the groupings list 334 and the interpretation rules 320.

A fifth step of the processing of the content to produce the knowledgeutilizing the confidence level includes integrating knowledge of thesurviving equation package into a knowledge base. For example,integrating at least a portion of the reduced OCA combinations into agraphical database to produce updated knowledge. As another example, theportion of the reduced OCA combinations may be translated into rows andcolumns entries when utilizing a rows and columns database rather than agraphical database. When utilizing the rows and columns approach for theknowledge base, subsequent access to the knowledge base may utilizestructured query language (SQL) queries.

As depicted in FIG. 18B, a specific example of the fifth step includesthe IEI control module 308 recovering fact base information 600 from SSmemory 96 to identify a portion of the knowledge base for potentialmodification utilizing the OCAs of the surviving interpretation 1030(i.e., compare a pattern of relationships between the OCAs of thesurviving interpretation 1030 from the interpreted information 344 torelationships of OCAs of the portion of the knowledge base includingpotentially new quality metric levels). The fifth step further includesdetermining modifications (e.g., additions, subtractions, furtherclarifications required when information is complex, etc.) to theportion of the knowledge base based on the new quality metric levels.For instance, the IEI control module 308 causes adding the element“black” as a “describes” relationship of an existing bat OCA and addingthe element “fruit” as a eats “does to” relationship to implement themodifications to the portion of the fact base information 600 to produceupdated fact base information 608 for storage in the SS memory 96.

FIG. 18C is a logic diagram of an embodiment of a method for processingcontent to produce knowledge within a computing system. In particular, amethod is presented for use in conjunction with one or more functionsand features described in conjunction with FIGS. 1-8D, 18A, and alsoFIG. 18B. The method includes step 1040 where a processing module of oneor more processing modules of one or more computing devices of thecomputing system identifies words of an ingested phrase to producetokenized words. The identified includes comparing words to known wordsof dictionary entries to produce identifiers of known words.

For each tokenized word, the method continues at step 1042 where theprocessing module identifies one or more identigens that corresponds tothe tokenized word, where each identigen describes one of an object, acharacteristic, and an action (e.g., OCA). The identifying includesperforming a lookup of identifiers of the one or more identigensassociated with each tokenized word, where the different identifiersassociated with each of the potential object, the characteristic, andthe action associated with the tokenized word.

The method continues at step 1044 where the processing module, for eachpermutation of sequential combinations of identigens, generates aplurality of equation elements to form a corresponding equation package.Each equation element describes a relationship between sequentiallylinked pairs of identigens, where each sequential linking pairs apreceding identigen to a next identigen. For example, for eachpermutation of identigens of each tokenized word, the processing modulegenerates the equation package to include a plurality of equationelements.

Each equation element describes the relationship (e.g., describes, actson, is a, belongs to, did, did too, etc.) between sequentially adjacentidentigens of a plurality of sequential combinations of identigens. Eachequation element may be further associated with a quality metric toevaluate a favorability level of an interpretation in light of thesequence of identigens of the equation package.

The method continues at step 1046 where the processing module selects asurviving equation package associated with most favorableinterpretation. For example, the processing module applies rules (i.e.,inference, pragmatic engine, utilizing the identifiers of the identigensto match against known valid combinations of identifiers of entigens),to reduce the number of permutations of the sequential combinations ofidentigens to identify at least one equation package.

Non-surviving equation packages are eliminated the compare unfavorablyto pairing rules and/or are associated with an unfavorable qualitymetric levels to produce a non-surviving interpretation. An overallquality metric level is assigned to each equation package based onquality metric levels of each pairing, such that a higher quality metriclevel indicates an equation package with a higher probability offavorability of correctness.

The method continues at step 1048 where the processing module integratesknowledge of the surviving equation package into a knowledge base. Forexample, the processing module integrates at least a portion of thereduced OCA combinations into a graphical database to produce updatedknowledge. The integrating may include recovering fact base informationfrom storage of the knowledge base to identify a portion of theknowledge base for potential modifications utilizing the OCAs of thesurviving equation package (i.e., compare a pattern of relationshipsbetween the OCAs of the surviving equation package to relationships ofthe OCAs of the portion of the knowledge base including potentially newquality metric levels).

The integrating further includes determining modifications (e.g.,additions, subtractions, further clarifications required when complexinformation is presented, etc.) to produce the updated knowledge basethat is based on fit of acceptable quality metric levels. Theintegrating further includes implementing the modifications to theportion of the fact base information to produce the updated fact baseinformation for storage in the portion of the knowledge base.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIGS. 19A and 19B are schematic block diagrams of another embodiment ofa computing system that includes that includes the content ingestionmodule 300 of FIG. 5E, the element identification module 302 of FIG. 5E,the interpretation module 304 of FIG. 5E, the answer resolution module306 of FIG. 5E, and the SS memory 96 of FIG. 2. Generally, an embodimentof this invention presents solutions where the computing system 10supports for generating a query response to a query.

The generating of the query response to the query includes a series ofsteps. For example, a first step includes identifying words of aningested query to produce tokenized words. As depicted in FIG. 19A, aspecific example of the first step includes the content ingestion module300 comparing words of query info 138 to dictionary entries to produceformatted content 314 that includes identifiers of known words. Forinstance, the content ingestion module 300 produces identifiers for eachword of the query “what black animal flies and eats fruit and insects?”

A second step of the generating of the query response to the queryincludes, for each tokenized word, identifying one or more identigensthat correspond the tokenized word, where each identigen describes oneof an object, a characteristic, and an action (OCA). As depicted in FIG.19A, a specific example of the second step includes the elementidentification module 302 performing a look up of identifiers, utilizingan element list 332 and in accordance with element rules 318, of the oneor more identigens associated with each tokenized word of the formattedcontent 314 to produce identified element information 340. A uniqueidentifier is associated with each of the potential object, thecharacteristic, and the action associated with a particular tokenizedword. For instance, the element identification module 302 produces asingle identigen identifier for each of the black color, an animal,flies, eats, fruit, and insects.

A third step of the generating of the query response to the queryincludes, for each permutation of sequential combinations of identigens,generating a corresponding equation package (i.e., candidateinterpretation), where the equation package includes a sequentiallinking of pairs of identigens. Each sequential linking pairs apreceding identigen to a next identigen. An equation element describes arelationship between paired identigens (OCAs) such as describes, actson, is a, belongs to, did, did to, etc.

As depicted in FIG. 19A, a specific example of the third step includesthe interpretation module 304, for each permutation of identigens ofeach tokenized word of the identified element information 340,generating the equation packages in accordance with interpretation rules320 and a groupings list 334 to produce a series of equation elementsthat include pairings of identigens. For instance, the interpretationmodule 304 generates a first pairing to describe a black animal, asecond pairing to describe an animal that flies, a third pairing todescribe flies and eats, a fourth pairing to describe eats fruit, and afifth pairing to describe eats fruit and insects.

A fourth step of the generating the query response to the query includesselecting a surviving equation package associated with a most favorableinterpretation. As depicted in FIG. 19A, a specific example of thefourth step includes the interpretation module 304 applying theinterpretation rules 320 (i.e., inference, pragmatic engine, utilizingthe identifiers of the identigens to match against known validcombinations of identifiers of entigens) to reduce the number ofpermutations of the sequential combinations of identigens to produceinterpreted information 344 that includes identification of at least oneequation package as a surviving interpretation 1060. Non-survivingequation packages, if any, are eliminated that compare unfavorably topairing rules to produce a non-surviving interpretation.

A fifth step of the generating the query response to the query includesutilizing a knowledge base, generating a query response to the survivingequation package of the query. The surviving equation package of thequery is transformed to produce query knowledge for comparison to aportion of the knowledge base, and where an answer is extracted from theportion of the knowledge base to produce the query response.

As depicted in FIG. 19B, a specific example of the fifth step includesthe answer resolution module 306 interpreting the survivinginterpretation 1060 of the interpreted information 344 in accordancewith answer rules 322 to produce query knowledge 1070 (i.e., a graphicalrepresentation of knowledge when the knowledge base utilizes a graphicaldatabase). The fifth step further includes accessing fact baseinformation 600 from the SS memory 96 to identify the portion of theknowledge base associated with a favorable comparison of the queryknowledge (e.g., by comparing attributes of the query knowledge 1072attributes of the fact base information 600). The fifth step furtherincludes generating preliminary answers 354 that includes the answer tothe query. For instance, the answer is bat when the associated OCAs ofbat, such as black, eats fruit, eats insects, is an animal, and flies,aligns with OCAs of the query knowledge.

FIG. 19C is a logic diagram of an embodiment of a method for generatinga query response to a query utilizing groupings within a knowledge basewithin a computing system. In particular, a method is presented for usein conjunction with one or more functions and features described inconjunction with FIGS. 1-8D, 19A, and also FIG. 19B. The method includesstep 1080 where a processing module of one or more processing modules ofone or more computing devices of the computing system identifies wordsof an ingested query to produce tokenized words. For example, theprocessing module compares words to known words of dictionary entries toproduce identifiers of known words.

For each tokenized word, the method continues at step 1082 where theprocessing module identifies one or more identigens that correspond tothe tokenized word, where each identigen describes one of an object, acharacteristic, and an action. For example, the processing moduleperforms a lookup of identifiers of the one or more identigensassociated with each tokenized word, where different identifiersassociated with each permutation of a potential object, characteristic,and action associated with the tokenized word.

For each permutation of sequential combinations of identigens, themethod continues at step 1084 where the processing module generates aplurality of equation elements to form a corresponding equation package.Each equation element describes a relationship between sequentiallylinked pairs of identigens. Each sequential linking pairs a precedingidentigen to a next identigen. For example, for each permutation ofidentigens of each tokenized word, the processing module includes, withall other permutations of all other tokenized words, to generate theequation packages, where each equation package includes a plurality ofequation elements describing the relationships between sequentiallyadjacent identigens of a plurality of sequential combinations ofidentigens.

The method continues at step 1086 where the processing module selects asurviving equation package associated with a most favorableinterpretation. For example, the processing module applies rules (i.e.,inference, pragmatic engine, utilizing the identifiers of the identigensto match against known valid combinations of identifiers of entigens) toreduce the number of permutations of the sequential combinations ofidentigens to identify at least one equation package. Non-survivingequation packages are eliminated the compare unfavorably to pairingrules.

The method continues at step 1088 where the processing module generatesa query response to the surviving equation package, where the survivingequation package is transformed to produce query knowledge for locatingthe portion of a knowledge base that includes an answer. As an exampleof generating the query response, the processing module interprets thesurviving the equation package in accordance with answer rules toproduce the query knowledge (e.g., a graphical representation ofknowledge when the knowledge base utilizes a graphical database format).

The processing module accesses fact base information from the knowledgebase to identify the portion of the knowledge base associated with afavorable comparison of the query knowledge (e.g., favorable comparisonof attributes of the query knowledge to the portion of the knowledgebase, aligning favorably comparing entigens without conflictingentigens). The processing module extracts an answer from the portion ofthe knowledge base to produce the query response.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

It is noted that terminologies as may be used herein such as bit stream,stream, signal sequence, etc. (or their equivalents) have been usedinterchangeably to describe digital information whose contentcorresponds to any of a number of desired types (e.g., data, video,speech, text, graphics, audio, etc. any of which may generally bereferred to as ‘data’).

As may be used herein, the terms “substantially” and “approximately”provides an industry-accepted tolerance for its corresponding termand/or relativity between items. For some industries, anindustry-accepted tolerance is less than one percent and, for otherindustries, the industry-accepted tolerance is 10 percent or more. Otherexamples of industry-accepted tolerance range from less than one percentto fifty percent. Industry-accepted tolerances correspond to, but arenot limited to, component values, integrated circuit process variations,temperature variations, rise and fall times, thermal noise, dimensions,signaling errors, dropped packets, temperatures, pressures, materialcompositions, and/or performance metrics. Within an industry, tolerancevariances of accepted tolerances may be more or less than a percentagelevel (e.g., dimension tolerance of less than +/−1%). Some relativitybetween items may range from a difference of less than a percentagelevel to a few percent. Other relativity between items may range from adifference of a few percent to magnitude of differences.

As may also be used herein, the term(s) “configured to”, “operablycoupled to”, “coupled to”, and/or “coupling” includes direct couplingbetween items and/or indirect coupling between items via an interveningitem (e.g., an item includes, but is not limited to, a component, anelement, a circuit, and/or a module) where, for an example of indirectcoupling, the intervening item does not modify the information of asignal but may adjust its current level, voltage level, and/or powerlevel. As may further be used herein, inferred coupling (i.e., where oneelement is coupled to another element by inference) includes direct andindirect coupling between two items in the same manner as “coupled to”.

As may even further be used herein, the term “configured to”, “operableto”, “coupled to”, or “operably coupled to” indicates that an itemincludes one or more of power connections, input(s), output(s), etc., toperform, when activated, one or more its corresponding functions and mayfurther include inferred coupling to one or more other items. As maystill further be used herein, the term “associated with”, includesdirect and/or indirect coupling of separate items and/or one item beingembedded within another item.

As may be used herein, the term “compares favorably”, indicates that acomparison between two or more items, signals, etc., provides a desiredrelationship. For example, when the desired relationship is that signal1 has a greater magnitude than signal 2, a favorable comparison may beachieved when the magnitude of signal 1 is greater than that of signal 2or when the magnitude of signal 2 is less than that of signal 1. As maybe used herein, the term “compares unfavorably”, indicates that acomparison between two or more items, signals, etc., fails to providethe desired relationship.

As may be used herein, one or more claims may include, in a specificform of this generic form, the phrase “at least one of a, b, and c” orof this generic form “at least one of a, b, or c”, with more or lesselements than “a”, “b”, and “c”. In either phrasing, the phrases are tobe interpreted identically. In particular, “at least one of a, b, and c”is equivalent to “at least one of a, b, or c” and shall mean a, b,and/or c. As an example, it means: “a” only, “b” only, “c” only, “a” and“b”, “a” and “c”, “b” and “c”, and/or “a”, “b”, and “c”.

As may also be used herein, the terms “processing module”, “processingcircuit”, “processor”, “processing circuitry”, and/or “processing unit”may be a single processing device or a plurality of processing devices.Such a processing device may be a microprocessor, micro-controller,digital signal processor, microcomputer, central processing unit, fieldprogrammable gate array, programmable logic device, state machine, logiccircuitry, analog circuitry, digital circuitry, and/or any device thatmanipulates signals (analog and/or digital) based on hard coding of thecircuitry and/or operational instructions. The processing module,module, processing circuit, processing circuitry, and/or processing unitmay be, or further include, memory and/or an integrated memory element,which may be a single memory device, a plurality of memory devices,and/or embedded circuitry of another processing module, module,processing circuit, processing circuitry, and/or processing unit. Such amemory device may be a read-only memory, random access memory, volatilememory, non-volatile memory, static memory, dynamic memory, flashmemory, cache memory, and/or any device that stores digital information.Note that if the processing module, module, processing circuit,processing circuitry, and/or processing unit includes more than oneprocessing device, the processing devices may be centrally located(e.g., directly coupled together via a wired and/or wireless busstructure) or may be distributedly located (e.g., cloud computing viaindirect coupling via a local area network and/or a wide area network).Further note that if the processing module, module, processing circuit,processing circuitry and/or processing unit implements one or more ofits functions via a state machine, analog circuitry, digital circuitry,and/or logic circuitry, the memory and/or memory element storing thecorresponding operational instructions may be embedded within, orexternal to, the circuitry comprising the state machine, analogcircuitry, digital circuitry, and/or logic circuitry. Still further notethat, the memory element may store, and the processing module, module,processing circuit, processing circuitry and/or processing unitexecutes, hard coded and/or operational instructions corresponding to atleast some of the steps and/or functions illustrated in one or more ofthe Figures. Such a memory device or memory element can be included inan article of manufacture.

One or more embodiments have been described above with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claims. Further, the boundariesof these functional building blocks have been arbitrarily defined forconvenience of description. Alternate boundaries could be defined aslong as the certain significant functions are appropriately performed.Similarly, flow diagram blocks may also have been arbitrarily definedherein to illustrate certain significant functionality.

To the extent used, the flow diagram block boundaries and sequence couldhave been defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claims. One of average skill in the art will alsorecognize that the functional building blocks, and other illustrativeblocks, modules and components herein, can be implemented as illustratedor by discrete components, application specific integrated circuits,processors executing appropriate software and the like or anycombination thereof.

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with one or more other routines. In addition, a flow diagrammay include an “end” and/or “continue” indication. The “end” and/or“continue” indications reflect that the steps presented can end asdescribed and shown or optionally be incorporated in or otherwise usedin conjunction with one or more other routines. In this context, “start”indicates the beginning of the first step presented and may be precededby other activities not specifically shown. Further, the “continue”indication reflects that the steps presented may be performed multipletimes and/or may be succeeded by other activities not specificallyshown. Further, while a flow diagram indicates a particular ordering ofsteps, other orderings are likewise possible provided that theprinciples of causality are maintained.

The one or more embodiments are used herein to illustrate one or moreaspects, one or more features, one or more concepts, and/or one or moreexamples. A physical embodiment of an apparatus, an article ofmanufacture, a machine, and/or of a process may include one or more ofthe aspects, features, concepts, examples, etc. described with referenceto one or more of the embodiments discussed herein. Further, from figureto figure, the embodiments may incorporate the same or similarly namedfunctions, steps, modules, etc. that may use the same or differentreference numbers and, as such, the functions, steps, modules, etc. maybe the same or similar functions, steps, modules, etc. or differentones.

Unless specifically stated to the contra, signals to, from, and/orbetween elements in a figure of any of the figures presented herein maybe analog or digital, continuous time or discrete time, and single-endedor differential. For instance, if a signal path is shown as asingle-ended path, it also represents a differential signal path.Similarly, if a signal path is shown as a differential path, it alsorepresents a single-ended signal path. While one or more particulararchitectures are described herein, other architectures can likewise beimplemented that use one or more data buses not expressly shown, directconnectivity between elements, and/or indirect coupling between otherelements as recognized by one of average skill in the art.

The term “module” is used in the description of one or more of theembodiments. A module implements one or more functions via a device suchas a processor or other processing device or other hardware that mayinclude or operate in association with a memory that stores operationalinstructions. A module may operate independently and/or in conjunctionwith software and/or firmware. As also used herein, a module may containone or more sub-modules, each of which may be one or more modules.

As may further be used herein, a computer readable memory includes oneor more memory elements. A memory element may be a separate memorydevice, multiple memory devices, or a set of memory locations within amemory device. Such a memory device may be a read-only memory, randomaccess memory, volatile memory, non-volatile memory, static memory,dynamic memory, flash memory, cache memory, and/or any device thatstores digital information. The memory device may be in a form asolid-state memory, a hard drive memory, cloud memory, thumb drive,server memory, computing device memory, and/or other physical medium forstoring digital information.

While particular combinations of various functions and features of theone or more embodiments have been expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent disclosure is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. A method for execution by a computing device, themethod comprises: detecting a defective entigen group, wherein aknowledge database includes the defective entigen group, wherein thedefective entigen group includes a plurality of entigens and one or moreentigen relationships between at least some of the plurality ofentigens, wherein the defective entigen group represents knowledge of atopic; obtaining corrective content for the topic based on the defectiveentigen group; generating a corrective entigen group based on thecorrective content; and updating the defective entigen group utilizingthe corrective entigen group to produce a curated entigen group.
 2. Themethod of claim 1 further comprises: storing the curated entigen groupin the knowledge database.
 3. The method of claim 1, wherein thedetecting the defective entigen group comprises one or more of:determining that a number of entigens of the defective entigen groupcompares unfavorably to a number of other entigens of another entigengroup associated with other knowledge of another topic, wherein theknowledge database further includes the other entigen group; identifyingan incorrect entigen of the defective entigen group; and identifying anincorrect entigen relationship between first and second entigens of thedefective entigen group.
 4. The method of claim 1, wherein the obtainingthe corrective content for the topic based on the defective entigengroup comprises: identifying a defect of the defective entigen group;identifying a content aspect based on the defect; selecting a contentsource based on the content aspect; and obtaining the corrective contentfrom the content source based on the content aspect.
 5. The method ofclaim 1, wherein the generating the corrective entigen group based onthe corrective content comprises: identifying a set of identigens foreach word of the corrective content to produce a plurality of sets ofidentigens, wherein a set of identigens of the plurality of sets ofidentigens represents one or more different meanings of a word of thecorrective content; and identifying one valid identigen of each set ofidentigens of the plurality of sets of identigens by applying identigenrules to the plurality of sets of identigens to produce the correctiveentigen group, wherein the corrective entigen group represents a mostlikely meaning of the corrective content.
 6. The method of claim 1,wherein the updating the defective entigen group utilizing thecorrective entigen group to produce the curated entigen group comprisesone or more of: replacing an incorrect entigen of the defective entigengroup with a correct entigen of the corrective entigen group; updatingan incorrect entigen relationship between first and second entigens ofthe defective entigen group with a correct entigen relationship betweenthe first and second entigens of the corrective entigen group; andaugmenting the defective entigen group utilizing the corrective entigengroup.
 7. A computing device of a computing system, the computing devicecomprises: an interface; a local memory; and a processing moduleoperably coupled to the interface and the local memory, wherein theprocessing module functions to: detect a defective entigen group,wherein a knowledge database includes the defective entigen group,wherein the defective entigen group includes a plurality of entigens andone or more entigen relationships between at least some of the pluralityof entigens, wherein the defective entigen group represents knowledge ofa topic; obtain, via the interface, corrective content for the topicbased on the defective entigen group; generate a corrective entigengroup based on the corrective content; and update the defective entigengroup utilizing the corrective entigen group to produce a curatedentigen group.
 8. The computing device of claim 7, wherein theprocessing module further functions to: store, via the interface, thecurated entigen group in the knowledge database.
 9. The computing deviceof claim 7, wherein the processing module functions to detect thedefective entigen group by one or more of: determining that a number ofentigens of the defective entigen group compares unfavorably to a numberof other entigens of another entigen group associated with otherknowledge of another topic, wherein the knowledge database furtherincludes the other entigen group; identifying an incorrect entigen ofthe defective entigen group; and identifying an incorrect entigenrelationship between first and second entigens of the defective entigengroup.
 10. The computing device of claim 7, wherein the processingmodule functions to obtain the corrective content for the topic based onthe defective entigen group by: identifying a defect of the defectiveentigen group; identifying a content aspect based on the defect;selecting a content source based on the content aspect; and obtaining,via the interface, the corrective content from the content source basedon the content aspect.
 11. The computing device of claim 7, wherein theprocessing module functions to generate the corrective entigen groupbased on the corrective content by: identifying a set of identigens foreach word of the corrective content to produce a plurality of sets ofidentigens, wherein a set of identigens of the plurality of sets ofidentigens represents one or more different meanings of a word of thecorrective content; and identifying one valid identigen of each set ofidentigens of the plurality of sets of identigens by applying identigenrules to the plurality of sets of identigens to produce the correctiveentigen group, wherein the corrective entigen group represents a mostlikely meaning of the corrective content.
 12. The computing device ofclaim 7, wherein the processing module functions to update the defectiveentigen group utilizing the corrective entigen group to produce thecurated entigen group by one or more of: replacing an incorrect entigenof the defective entigen group with a correct entigen of the correctiveentigen group; updating an incorrect entigen relationship between firstand second entigens of the defective entigen group with a correctentigen relationship between the first and second entigens of thecorrective entigen group; and augmenting the defective entigen grouputilizing the corrective entigen group.
 13. A computer readable memorycomprises: a first memory element that stores operational instructionsthat, when executed by a processing module, causes the processing moduleto: detect a defective entigen group, wherein a knowledge databaseincludes the defective entigen group, wherein the defective entigengroup includes a plurality of entigens and one or more entigenrelationships between at least some of the plurality of entigens,wherein the defective entigen group represents knowledge of a topic; asecond memory element that stores operational instructions that, whenexecuted by the processing module, causes the processing module to:obtain corrective content for the topic based on the defective entigengroup; a third memory element that stores operational instructions that,when executed by the processing module, causes the processing module to:generate a corrective entigen group based on the corrective content; anda fourth memory element that stores operational instructions that, whenexecuted by the processing module, causes the processing module to:update the defective entigen group utilizing the corrective entigengroup to produce a curated entigen group.
 14. The computer readablememory of claim 13 further comprises: a fifth memory element that storesoperational instructions that, when executed by the processing module,causes the processing module to: store the curated entigen group in theknowledge database.
 15. The computer readable memory of claim 13,wherein the processing module functions to execute the operationalinstructions stored by the first memory element to cause the processingmodule to detect the defective entigen group by one or more of:determining that a number of entigens of the defective entigen groupcompares unfavorably to a number of other entigens of another entigengroup associated with other knowledge of another topic, wherein theknowledge database further includes the other entigen group; identifyingan incorrect entigen of the defective entigen group; and identifying anincorrect entigen relationship between first and second entigens of thedefective entigen group.
 16. The computer readable memory of claim 13,wherein the processing module functions to execute the operationalinstructions stored by the second memory element to cause the processingmodule to obtain the corrective content for the topic based on thedefective entigen group by: identifying a defect of the defectiveentigen group; identifying a content aspect based on the defect;selecting a content source based on the content aspect; and obtainingthe corrective content from the content source based on the contentaspect.
 17. The computer readable memory of claim 13, wherein theprocessing module functions to execute the operational instructionsstored by the third memory element to cause the processing module togenerate the corrective entigen group based on the corrective contentby: identifying a set of identigens for each word of the correctivecontent to produce a plurality of sets of identigens, wherein a set ofidentigens of the plurality of sets of identigens represents one or moredifferent meanings of a word of the corrective content; and identifyingone valid identigen of each set of identigens of the plurality of setsof identigens by applying identigen rules to the plurality of sets ofidentigens to produce the corrective entigen group, wherein thecorrective entigen group represents a most likely meaning of thecorrective content.
 18. The computer readable memory of claim 13,wherein the processing module functions to execute the operationalinstructions stored by the fourth memory element to cause the processingmodule to update the defective entigen group utilizing the correctiveentigen group to produce the curated entigen group by one or more of:replacing an incorrect entigen of the defective entigen group with acorrect entigen of the corrective entigen group; updating an incorrectentigen relationship between first and second entigens of the defectiveentigen group with a correct entigen relationship between the first andsecond entigens of the corrective entigen group; and augmenting thedefective entigen group utilizing the corrective entigen group.